Main Responsibilities and Required Skills for Data Scientist
A data scientist uses data and statistics to solve a problem and make better decisions about their business processes. They are often involved in data collection and analysis in order to identify patterns and trends. In this blog post we describe the primary responsibilities and the most in-demand hard and soft skills for Data Scientists.
Get market insights and compare skills for other jobs here.
Main Responsibilities of Data Scientist
The following list describes the typical responsibilities of a Data Scientist:
Analyse
Analyse key performance indicators such as retention, player lifetime value and acquisition cost.
Analyse large, raw, and potentially noisy datasets to derive actionable intelligence.
Analyse the data we already hold on our users and find new data sources.
Analyze and extract insights from internal and external data.
Analyze data to discover hidden trends.
Analyze forecast model results and determine ways to improve statistical forecast as needed.
Analyze the impact of recently deployed features to determine their impact on user engagement.
Analyze trends in data to identify growth opportunities for our business.
Answer
Answer in-depth analytics questions from the team and create reports of the results.
Anticipate
Anticipate and prevent problems and roadblocks before they occur.
Apply
Apply data visualization techniques to present the results of statistical analysis.
Assess
Assess current dataflow, provide recommendations for data ingestion.
Assess the effectiveness and accuracy of new data sources and data gathering techniques.
Assist in
Assist in developing improved data science methodology in relation to healthcare data.
Assist in managing and contributing to product development.
Assist with buy-vs-build determinations.
Assist with the design, development, and maintenance of the Analytic DataMart.
Assure
Assure quality control (clean code culture).
Attend
Attend, present or support conferences and seminars.
Audit
Audit and maintain business listings and location directories across the web.
Automate
Automate data pipelines containing large volumes of data using SQL or Python based ETL Frameworks.
Build
Build actionable data reports and visualizations that will help other teams make strategic decisions.
Build algorithms and predictive models including regression, random forest, clustering, etc..
Build analytic models and iterate to help demonstrate the business case to scale the solution.
Build and construct prototypes of Advanced Analytic work-flows.
Build and improve data ETL pipeline to collect new data and refine the existing data sources.
Build and maintain relationships across broad geographies and time zones.
Build and prototype analysis pipeline iteratively to provide insights at scale.
Build and support data and machine learning models related to business applications.
Build customized dashboards for internal and external clients.
Build data solutions including search, machine learning models and knowledge graphs.
Build / deployment system management (a.k.a.
Build features into our application to allow for actionable insights from predictive model outputs.
Build ground-up exploratory machine vision models.
Build machine learning systems to understand the cross-channel grocery ecosystem.
Build microservice-based data processing pipeline and intelligent services.
Build models and tools that help the business increase market share.
Build models which enable us to assess and optimise marketing and promotion actions.
Build predictive models on price and vacancy rate.
Build predictive models to forecast upstream and downstream market activities.
Build predictive models to influence business and product decisions.
Build and optimizes classifiers using machine learning techniques.
Build tools and visualization dashboards to automate data analysis processes.
Build trust and credibility with stakeholders.
Build up ground truth data set.
Calculate
Calculate model impact / return on investment and communicate value to partners.
Capture
Capture and analyze information or data on current and future trends.
Capture and understand the business need, converting them into data driven options to be solutioned.
Capture the insights to stand for investors.
Champion
Champion Data Science principles throughout the wider business.
Collaborate with
Collaborate with cross functional teams to incorporate content, fitness, and music data.
Collaborate with management to prioritize and information needs.
Collaborate with ML engineers to test and deploy ML prototypes to production environments.
Collaborate with team day to day Kanban activities and design skills.
Collaborate with the Product Team on requirements and priorities.
Collect
Collect data from various sources such as web APIs to internal databases encoded in SQL.
Collect, reduce, process, and present model input and output data.
Communicate
Communicate effectively with team members, management, and clients.
Communicate results and ideas to key decision makers.
Communicate results / findings with the client and identify implications and recommend actions.
Communicate strategies, ideas, goals, and progress to the customer and the team.
Communicate to stakeholders outside the team project status & risks.
Communicate your findings and recommendations clearly and concisely with business stakeholders.
Compare
Compare validation results with model developer results for replicability.
Conceptualize
Conceptualize, code, and implement new analytic processes for IMFS using Big Data technologies.
Conduct
Conduct an ad-hoc analysis as requested to dig into product performance.
Conduct analysis to understand what experience characteristics are driving customer satisfaction.
Conduct concept and usability testing and gather feedback.
Conduct experiments to show causal impact of new ideas or implementations.
Conduct methodology and code reviews.
Conduct research and prototyping innovations.
Conduct validation studies to benchmark and improve the accuracy of Oura's biomarkers and algorithms.
Construct
Construct customer-facing reporting tools to provide insights and metrics which track forecast.
Consult with
Consult with other teams on applications of data science.
Consult with other teams within BlackBerry on applications of data science.
Contribute to
Contribute data science expertise to R& D projects, in collaboration with R& D team members.
Contribute to and drive best practices and processes in ML at the team and organizational level.
Contribute to delivery of technical capability innovation.
Contribute to knowledge sharing and foster partnerships with the group's teams and offices.
Contribute to malaria SME capacity-building efforts for national malaria control programs.
Contribute to new business proposals and proposal development.
Contribute to site digital project.
Contribute to the development and implementation of consolidated analytics capabilities.
Coordinate
Coordinate the creation, validation, and use of models.
Coordinate the system validation of enterprise analytical tools.
Coordinate the utilization of external vendors to deliver AI / ML solutions.
Coordinate with the team and get the requirements.
Create
Create and maintain optimal data and model dataOps pipeline architecture.
Create and present materials on data projects, data science architecture and analytics strategies.
Create and update regular reporting based on a variety of data sources.
Create an inquisitive, data-centric culture around the business.
Create any software code necessary for the use of the proposed methodology.
Create a single source of truth by consolidating our various data models and queries.
Create clear, concise, and visually appealing data dashboards and visualizations.
Create compelling presentations that provide actionable insights and recommendations.
Create dashboards that empower tech leadership to measure progress and prioritize action.
Create data pipelines for curating sets of features and variables for analysis.
Create ETL and data transformation internal libraries.
Create Power BI reporting from various sources of data.
Create scalable data pipelines and machine learning systems.
Create scalable models that drive business decisions and enable our customers' success.
Create visualizations to aid in understanding data.
Cultivate
Cultivate and maintain the integrity of data.
Customize
Customize this if you have information specific to the client's benefit package.
Define
Define and report on metrics and KPIs.
Define data science goals in conjunction with customers and our business teams.
Define key metrics to help track our business performance and customer experience.
Define the clinical domain specific schema for annotation in Radiology, Cardiology and Oncology.
Define the education experience of the future.
Deliver
Deliver a high level of accuracy.
Design
Design and build new data set processes for modeling, data mining, and production purposes.
Design and develop project prototypes and solutions.
Design and evaluate novel scalable approaches to experimentation.
Design and implement AI and Machine Learning models.
Design and implement automated, scalable NLP models for accessing and presenting information.
Design and implement data reports and dashboards.
Design and implement statistical data quality procedures for new data sources.
Design and implement tools for monitoring and forecasting trends in signals derived from text data.
Design and validate statistical experiments.
Design data strategies to help you or your organisation extract value from data.
Design data structures for efficient data manipulation.
Design econometric models, and supervised and unsupervised learning approaches.
Determine
Determine and maintain correctness of data (data quality).
Determine and procure tooling / platform.
Determine best price to sell the insurance product).
Determine new experimentation methods and statistical techniques to design solutions.
Determine priorities for experiments.
Develop
Develop advanced analytics models in order to maximize ROI, revenue and overall profitability.
Develop algorithms / software for accessing and handling data appropriately.
Develop and drive innovative data mining thinking, including trend analysis.
Develop and evaluate machine learning models to enhance Ripjar's software and data products.
Develop and execute insights that inform decision making around our people, culture, and leadership.
Develop and implement a machine-learning pipeline and its automation.
Develop and implement marketing data management practices.
Develop and implement new analytical methodologies and techniques for clients.
Develop and maintain long- term stakeholder relationships and networks.
Develop and monitor acquisition strategies for prospects from face to face and digital channels.
Develop and tests medium to complex statistical or computational models.
Develop data pipelines to efficiently extract insights.
Develop data science solutions as currently defined by the existing product roadmap.
Develop efficient and accurate analytical models that mimic business decisions.
Develop figures, tables, and reports and participate in manuscript writing.
Develop forecasting models for various aspects of the business.
Develop innovative analysis techniques.
Develop internal tools and codebases that are useful for other Data Scientists and / or Engineers.
Develop new and emerging data science capabilities within the team.
Develop new climate risk estimation methods at the forefront of science and technology.
Develop our Music data knowledge and grow the power of Music in our business and for our customers.
Develop predictive models through all stages in the product life cycle.
Develop processes and tools to monitor and analyse model performance and data accuracy.
Develop processes to consolidate and analyze structured and unstructured data.
Develop R&D work around career and learning data.
Develop statistical analysis, and models and apply them to solve real problems.
Develop style of the presentation for existing concepts / directions.
Develop technical solutions for operational effectiveness and excellence.
Develop visualizations to make your complex analyses accessible to a broad audience.
Develop visual reports, KPI scorecards, and dashboards using Power BI desktop.
Diagnose
Diagnose and troubleshoot database error.
Dig
Dig through the data whether it resides inside Salesforce or outside in third-party systems.
Disseminate
Disseminate the findings from analysis and experiments for easier stakeholder consumption.
Document
Document data and data product decisions and implementation details.
Document detailed designs with class and sequence diagrams.
Document Structure Comprehension.
Drive
Drive and develop the customer journey analytics across enablement experiences.
Drive and empower game studio to make quantitatively informed, evidence based decisions.
Drive high-visibility and high-impact projects.
Drive the testing automation capabilities.
Encourage
Encourage boundary breaking by challenging colleagues to question established work processes.
Engage in
Engage in ongoing discussions and moves others towards a position.
Engage with global counterparts to share learnings.
Ensure
Ensure adoption and replication of solutions delivered through Analytics globally within Rio Tinto.
Ensure AI and machine learning production pipelines are scalable, repeatable and secure.
Ensure continued high retention rates in an assigned book of business.
Ensure data integrity and accuracy across all model inputs.
Ensure our data and analysis are reliable and rigorous across all of our products.
Ensure statistical processing of data (generating frequencies, cross-tabulations).
Ensure the Edmit database maintains best practices and is free of errors or extraneous / old data.
Ensure the technical feasibility of UI / UX.
Establish
Establish and maintain a role as advisor to clients and colleagues.
Establish best practices around ML production infrastructure.
Evaluate
Evaluate accuracy and quality of data sources, as well as the designed models.
Evaluate and design experiments to monitor key metrics and identify improvement opportunities.
Evaluate and experiment with new technologies and tools prior to wider adoption by the team.
Evaluate and test machine learning methods and maintain a high level of quality.
Evaluate existing predictive analytics tools / functions and / or develop new approaches and methods.
Evangelize
Evangelize insights through storytelling and make specific product recommendations to teams.
Execute
Execute on a plan for improving user outcomes with our analytics and ML.
Explain
Explain technical issues and data solution strategies to stakeholders and other members of the team.
Explore
Explore and develop new technologies as part of the core tech organization.
Explore, generate & source data relevant to the hypothesis (internal, external, 3rd party).
Explore new capabilities and technologies to drive innovation to scale the journey science platform.
Explore opportunities to re-position existing drugs in novel indications.
Extend
Extend existing ML libraries and frameworks when necessary.
Facilitate
Facilitate and monitor daily activities of clinical trials or research projects.
Feed
Feed into the creation of internal data science tooling.
Filter
Filter, sort, sanitize, and "clean” data to be programmatically utilized.
Find
Find creative ways to solve UX problems (e.g. usability, findability).
Find ways to solve business problems with data.
Focus
Focus on a variety of areas that support the business including.
Focus on longer-range planning for functional area (e.g. 12 months or greater).
Focus on reliability and resilience to failure in service design.
Gather
Gather stakeholder requirements to design experiments and then deliver KPI and ROI numbers.
Generate
Generate the reporting / alerts for internal forecast review.
Guide
Guide and lead the development teams on implementation and improving the product.
Guide the implementation of web analytics by engineering teams.
Handle
Handle potentially incomplete datasets.
Harden
Harden models for production deployment and monitor model performance over time.
Help
Help defining and maintaining data related policies, data definitions, provenance, and quality rules.
Help distill complex machine learning products to various audiences.
Help to cultivate an environment that promotes excellence, innovation, and a collegial spirit.
Help us improve the health and well-being of billions of people, every year.
Identify
Identify, analyse, and interpret trends or patterns in a variety of internal and external sources.
Identify and translate business problems into deliverable research problems.
Identify globally implemented reuse cases for local implementation.
Identify modeling issues, design new features, and continuously provide ideas to improve our models.
Identify opportunities for Machine Learning applications within the existing Products.
Identify, retrieve, and manipulate data from internal and external datasets.
Implement
Implement AI / ML algorithms relevant to healthcare and communications systems.
Implement and test machine learning models with Zynga's games that impact millions of users.
Implement and validate predictive models.
Improve
Improve complex data flow, data structures and database design to move to next platform.
Improve keyword extraction from text documents.
Improve overall quality by enhancing optimization, tooling, and testing.
Improve real-time rep and set detection from time-series data.
Initiate
Initiate and define new business opportunities and products.
Interact with
Interact with the Product team and / or the client to solve real business problems.
Interface with
Interface with business and customer teams to define, and track goals.
Interface with clients to gather data and present results of studies and analyses.
Interface with UX team to translate prototype visualizations into web applications.
Interpret
Interpret and present modeling and analytical results to a non-technical audience.
Interpret business trends and make recommendations to increase value of company.
Interpret internal or external business issues and recommends best practices.
Investigate
Investigate and perform deeper analysis to produce impactful algorithms to achieve targeted outcomes.
Iterate
Iterate and improve the model performances using appropriate transfer learning techniques.
Keep
Keep a positive attitude when problems arise.
Keep a view on development and other capabilities across JLL.
Keep current with industry and academic developments.
Lead
Lead and drive in deploy and testing of the solutions and insights.
Lead and implement action plans for data processing and model implementation.
Lead and support the internal Data Science Challenge and other Group-sponsored events.
Lead cross-team cooperation and mentor junior scientists.
Lead Data Scientist 3, Words with Friends.
Lead lead functional teams or projects working with SPG Product Groups and Support Orgs.
Lead our data science team to build out our core image analysis algorithm.
Lead large, complex projects to achieve key business objectives.
Lead the design for application solutions that will solve data analytic needs.
Learn
Learn and adapt emerging machine learning methodologies to real problems.
Learn, implement and own machine learning models for personalization and product recommendations.
Leverage
Leverage learning packages like Scikit-Learn and Tensorflow to develop Machine Learning models.
Leverage models to test effectiveness of different courses of action.
Leverage visualization tools / packages to create powerful representations of results.
Liase with
Liase with Product and IT teams to push through new products.
Maintain
Maintain accurate records of services performed using computer system.
Maintain a Github repository, actively participate in data science competitions, mentoring.
Maintain and expand your knowledge of ML / AI and current technology through training opportunities.
Maintain and optimize AI algorithms running in production, access and storage of data.
Make
Make actionable recommendations based on the findings.
Make it happen - We own things and get them done whatever it takes.
Make operational changes to improve customer experience.
Make recommendations for new metrics, techniques and strategies to improve Geotab's product suite.
Manage
Manage and solve a variety of internal technical requests.
Manage external analytical partners where required.
Manage model governance needs for business partners in ESI, EviCore and Vendors.
Manage work order tracker and timely closeout of all work orders by end of day.
Meet
Meet and collaborate with business users on requirements, objectives and metrics.
Mentor
Mentor data scientists throughout the project life cycle.
Model
Model creation, training and validation.
Model technique selection and integration.
Model Validation & Governance.
Monitor
Monitor key product metrics, understanding root causes of changes in metrics.
Monitor models in production and make recommendation when they should be rebuilt.
Operationalize
Operationalize the tracking of these metrics through self-service dashboards and other tools.
Optimize
Optimize our processes for extracting and reshaping data from external sources.
Optimize the data warehouse for analytical query loads.
Oversee
Oversee and / or independently perform tasks from end to end.
Oversee the development and delivery of tools and training for data and analytics.
Own
Own reproducible data science projects end-to-end.
Own and deliver projects of diverse scope.
Participate
Participate in data modeling implementation using statistical computing languages.
Participate in recruiting, interviewing, hiring, on-boarding, and mentoring of new team members.
Perform
Perform ad-hoc analysis and presents results in a clear manner.
Perform analytic requests utilizing Nielsen retail sales data as needed.
Perform Database Administration.
Perform data mining on large structured and unstructured datasets to uncover insights.
Perform econometric and statistical analysis on very large granular transaction level datasets.
Perform equipment installation and operational qualifications for cGMP cell processing instruments.
Perform initial data quality checks on extracted data.
Perform initial data quality checks on the extracted data.
Perform maintenance on large and complex research and administrative datasets.
Perform ML research, exploratory data analysis, and computer-vision modeling.
Perform other assigned duties as required.
Perform other duties as required.
Perform robust data analysis and communicate results with sophisticated visualisation methods.
Perform statistical analysis and fine-tuning using test results.
Perform tasks of data wrangling and feature engineering.
Perform technical risk analysis and reliability assessments.
Prepare
Prepare and obtain approval of system and programming documentation.
Prepare and train the model.
Prepare both structured and unstructured data for analysis.
Prepare comprehensive model development and deployment documentation.
Prepare documents for each language before sent off to agencies / linguists.
Present
Present business analysis results and recommendations to key stakeholders.
Present findings in a compelling way to influence Instacart's leadership.
Present the latest academic research papers to your team.
Process
Process control / optimization knowledge is desirable, but not mandatory.
Produce
Produce daily metrics and reports.
Produce deliverable results and see them through from development to production.
Produce high quality production-strength software and systems.
Produce quantitative and qualitative modeling of business dynamics, user behavior, etc..
Propose
Propose machine-learning and statistical models for practical applications.
Propose solutions and strategies to business challenges.
Propose technical design and lead the implementation of data solutions.
Prototype
Prototype data science algorithms supporting product feature development.
Prototype the algorithms for execution in Hadoop environment.
Provide
Provide databases to clients in different formats (ASCII, Excel, SPSS, SAS, etc.).
Provide deep knowledge and leadership in NLP / NLU statistical analysis as a thought leader.
Provide expert peer feedback through team meetings and code review.
Provide feedback on areas for development.
Provide forward looking data, information, and assessments in support of decision making.
Provide guidance regarding analytical approach and iteration of algorithms.
Provide guidance, support and mentoring to junior team members.
Provide input and advice to MI / BI teams as part of data roadmap with IT.
Provide interfaces for other groups to use the new tools with different technologies.
Provide project support relating to the development of AML Transaction Monitoring solutions.
Provide quantitative analysis and strategic insights to make smart, data-driven decisions.
Provide strategic input into business decisions as a trusted advisor.
Provide support for technical guidance and training of other team members.
Provide the supervisor with periodic progress reports.
Provide thought leadership on developing and executing data analysis protocols.
Provide thought leadership on the product technical roadmap and other technology areas.
Query
Query databases from multiple marketing and product sources and create usable KPI dashboards.
Recommend
Recommend creative solutions to achieve business goals.
Reduce
Reduce complexity for our customers and patients.
Represent
Represent your team in all agency meetings – presenting occasionally.
Research
Research and analyze large data sets using a variety of techniques.
Respond
Respond to workforce data inquiries with creative visual data presentations.
Review
Review deliverables of self and others team to ensure that they meet client expectations.
Review, evaluates, and derives requirements for testability.
Run
Run machine learning tests and experiments.
Seek
Seek opportunities for professional growth and expansion of consulting skills and experience.
Share
Share knowledge and learning with broader team.
Support
Support ad hoc reporting requirements and / or special projects.
Support and enhance the existing work on health user profiling, prediction, and targeting tools.
Support business decisions with ad hoc analysis as needed.
Support development of automated data ingest processing and applications.
Support development of novel genomic signatures.
Support end-to-end development of predictive model, monitor and calibrate production models.
Support in building up our data science environment on Azure.
Support junior data scientists' growth through mentoring and project pairing.
Support lab-based product and process characterization studies.
Support / lead campaign analytics and data processing execution.
Support more junior data scientists with Change Management.
Support operations with automated reporting and dashboards.
Support product managers by conducting preliminary & exploratory analysis towards roadmap items.
Support regular ad-hoc data and analysis needs to better understand customer behavior.
Support Stakeholders in identifying key initiatives and building cross functional business use.
Support Stakeholders in identifying key initiatives and building cross functional business use cases.
Support the execution of Data Science solutions to business problems.
Support the product development team in the creation of features for predictive ML models.
Support the team through reviewing work and mentoring others.
Take
Take initiative to validate and troubleshoot data and statistical models.
Take projects from ideation to data analysis to production and presentation.
Test
Test and learn, test and learn, test and learn, test and learn and so on.
Test the code using the appropriate testing approach.
Track
Track CRFs as they are processed through the Data Management department.
Track of record of delivering innovative analytical insights to a variety of customers.
Translate
Translate technical details, observations and results into business language.
Troubleshoot
Troubleshoot medium complexity problems.
Turn
Turn statistical and computational analysis into user-friendly graphs, charts, and animation.
Understand
Understand and explain model and automated decisions to business and technical stakeholders.
Understand and maintain data pipelines from raw sources to feature stores for models.
Understand and refine requirements.
Understand business needs to define data models / algorithm needs.
Understand data needs and construct data pipelines for automating and accelerating data preparation.
Understand drivers for health & wellness for both individuals and the health ecosystem at large.
Understand
Understand the end user and consumer of transformed data.
Understand which data is right for each use case.
Update
Update on meaningful developments and performance updates in a relevant, objective and timely manner.
Use
Use analytical and statistical tools to evaluate data and aid in decision making.
Use data to optimize our 7 figure monthly advertising budgets.
Use DWH and Big Data sources for data analytics exercises.
Use NLP techniques to parse, process.
Use of modern cloud data storage, access, and visualization methods.
Use predictive modelling to increase and optimise customer experiences and other business outcomes.
Use advanced data visualisation techniques to present analysis findings.
Use statistical methods to test findings through prototyping and scenario testing.
Utilise
Utilise user centred design methodologies to personalise and maximise end user experienceInformation.
Utilize statistical models, algorithms, machine learning, trend analysis, and predicative analysis.
Validate
Validate the quality of the analytical approach and project outputs of the other team members.
Visualize
Visualize and present insights and models clearly and effectively to diverse audiences.
Visualize data in way that tells compelling narratives.
Visualize results of statistical analyses in the form of graphs, charts, tables and scorecards.
Visualise and present insights and models clearly and effectively.
Work with
Work closely with data engineers, marketing and product teams to deliver insights and data services.
Work closely with engineering teams to develop a strategy for long term data platform architecture.
Work closely with IT to monitor system performance and problem solve when issues arise.
Work closely with other data scientists, developers, and product managers on R&D projects.
Work closely with senior leadership on significant projects.
Work closely with the leadership team to instill a data-driven culture.
Work cross functionally with vendors and teams to deliver exceptional analytical insights.
Work directly with the development teams on product instrumentation, product flow and data capture.
Work in a cross functional team leveraging Scrum and Agile methodologies.
Work in a multi-disciplinary scrum team to collect business data and build data pipelines.
Work methodically and accurately on any task given.
Work on analytics use cases ranging from prioritization, forecasting, to optimization.
Work on the improvement of existing Kpler proprietary algorithms.
Work together with data and product teams to feed the model with accurate and clean data set.
Work with business sponsors, AIG's Travel Partners and IT teams to implement analytic solutions.
Work with customers to understand in depth the data mobility and privacy problems they are facing.
Work with distributed computing tools (HDFS, Map / Reduce, Hive, Hbase, Sqoop, etc.).
Work with internal and external data scientists to scope and execute Advance Analytics projects.
Work with key NVIDIA customers in the Federal and Education areas.
Work with multiple complex data sources.
Work with new technologies and champion them in MI Communities within Ericsson.
Work with other team members to build analytics applications, visualizations, and APIs.
Work with product team to ensure that new data are available on time and can be surfaced to users.
Work with stakeholders' feedback to implement changes to reporting.
Work with the business domains to help shape the right questions.
Work with the data architect teams on Google Cloud Platform / Amazon AWS / Azure.
Work with the marketing team to design experiments and decide what data to gather.
Work with the various directorates and development teams to identify advanced analytics needs.
Work with third party music partners to improve music discovery and music metadata.
Write
Write and debug SQL queries.
Write and implement custom code for web scraping, text mining, data ingestion and record linkage.
Write and test complex predictive model software packages for production deployment.
Write code that is easily maintainable and highly reliable.
Write complex, maintainable code to develop.
Write production code at scale, testing and productizing new algorithms.
Write production grade machine learning code with a bias towards fast implementation.
Write specs and requirements related to the algorithms you develop for product development.
Most In-demand Hard Skills
The following list describes the most required technical skills of a Data Scientist:
R
Data Science
Mathematics
Spark
Tensorflow
Tableau
Deep Learning
AWS
Hadoop
Java
Pandas
Pytorch
Physics
SAS
Scikit-Learn
Scala
NLP
Hive
Economics
Analytics
Clustering
Numpy
Keras
Data Mining
Math
Natural Language Processing
Regression
Azure
Machine Learning Techniques
ML
GIT
Operations Research
AI
Programming Languages
Data Analysis
Data Visualization
Applied Mathematics
Neural Networks
Data Visualization Tools
Matlab
GCP
Coding
Artificial Intelligence
C++
Relational Databases
Big Data
Most In-demand Soft Skills
The following list describes the most required soft skills of a Data Scientist:
Written and oral communication skills
Problem-solving attitude
Analytical ability
Interpersonal skills
Attention to detail
Organizational capacity
Collaborative
Curious
Self-starter
Creative
Presentation
Team player
Critical thinker
Time-management
Leadership
Self-motivated
Work independently with little direction
Flexible
Teamwork
Proactive
Detail-oriented
Multi-task
Drive to learn
Quick learner
Bilingualism
Solve problems
Entrepreneurial mindset
Solve business problems
Commitment
Adaptable to changes
