Main Responsibilities and Required Skills for Lead Data Scientist
A Lead Data Scientist is a senior-level professional who manages data projects and leads a team of data scientists within an organization. They understand and analyze data to drive growth and plan and prioritize data projects. In this blog post we describe the primary responsibilities and the most in-demand hard and soft skills for Lead Data Scientists.
Get market insights and compare skills for other jobs here.
Main Responsibilities of Lead Data Scientist
The following list describes the typical responsibilities of a Lead Data Scientist:
Align
Align data projects with organizational goals.
Assess
Assess the effectiveness and accuracy of new data sources and data gathering techniques.
Assess the potential usefulness and validity of new data sources.
Balance
Balance team and individual responsibilities.
Build
Build analytic systems and predictive models.
Build customer focused Next Best Action, Attribution & Segmentation models.
Build, lead and orchestrate the global data science community.
Build, train and evaluate prototype models, using a tool or framework of choice.
Build visualisations to help non-technical stakeholders understand impact.
Coach
Coach, guide and direct teams of internal and vendor resources.
Co-create
Co-create with business / market / functions or IT platforms on requirements.
Collaborate with
Collaborate across IT platform teams to deploy solutions and drive continuous improvements.
Collaborate and share with the technical community while educate customers on data platform.
Collaborate on building efficient data pipelines and workflows with our engineering team.
Collaborate with key stakeholders to remove barriers to delivery.
Collaborate with other Data Scientists / Machine Learning Engineers and provide technical direction.
Collaborate with the Product Team on requirements and priorities.
Collaborate with translators to define technical problem statement and hypothesis to test.
Communicate
Communicate complex analytical findings and implications to business leaders.
Conceive
Conceive, plan and prioritize data projects.
Conduct
Conduct and lead meetings and workshops to explain findings.
Contribute to
Contribute to building a positive team spirit.
Contribute to problem solving within a flat team structure.
Craft
Craft effective campaign strategies to acquire users across a multitude of user acquisition channels.
Create
Create alternative model approaches to assess complex model design and advance future capabilities.
Create and design visuals across a wide range of on and offline channels.
Create compelling presentations that provide actionable insights and recommendations.
Create systems and analyses to derive insights that change the studio's world view.
Create unsupervised learning approaches for monitoring anomalous behavior in transactions.
Customize
Customize this if you have information specific to the client's benefit package.
Define
Define and develop a data migration strategy between different products and platforms.
Define data science goals in conjunction with customers and our business teams.
Design
Design and build data set processes for modeling, data mining, and production purposes.
Design and develop customized interactive reports and dashboards in Tableau.
Design and develop machine learning / artificial intelligence (ML / AI) algorithms.
Design and evaluate novel scalable approaches to experimentation.
Design and implement prototypes using Python and Java programming language.
Determine
Determine best format to execute ideas.
Determine new ways to improve data and search quality, and predictive capabilities.
Develop
Develop A / B testing frameworks and test model quality.
Develop and implement strategies to automate production of methodologies.
Develop and / or use algorithms and statistical predictive models, including advanced models.
Develop creative ways to identify and use data that significantly impact key business metrics.
Develop data-driven solutions to difficult business challenges.
Develop documentation to support objectives of operationally-focused team members.
Develop executive presentations with guidance.
Develop processes and tools to analyze and monitor model performance while ensuring data accuracy.
Develop prototypes, proof of concepts, algorithms, predictive models, and custom analysis.
Develop security compliant user management framework for multi-tenant big data platform.
Document
Document analytics, models and test methodologies.
Drive
Drive and empower game studio to make quantitatively informed, evidence based decisions.
Drive innovation by creating new frameworks, prototypes and automation projects in relevant areas.
Drive stakeholder engagements by driving complex analytical projects including bottoms-up projects.
Drive the execution of the team and provide visibility to the projects and progress.
Embody
Embody technical leadership, recommend and enforce best practices.
Engage
Engage with global counterparts to share learnings.
Enhance
Enhance data collection procedures to include relevant data for building analytical systems.
Ensure
Ensure consistency of Tech Accelerator standards within the squad.
Ensure data quality and integrity.
Ensure proper escalation, prioritization, and remediation of data quality issues.
Ensure quality of data and solution developed.
Ensure the confidentiality and integrity of the information being accessed.
Execute
Execute on a plan for improving user outcomes with our analytics and ML.
Explore
Explore and validate new data sources that improves the performance of Kettle's current system.
Gather
Gather, analyse, and model data to solve complex business problems.
Generate
Generate stock lists and place orders.
Grasp
Grasp requirements on call and deliver to specification.
Grow
Grow people and their talent to perfect the team's execution.
Guide
Guide test design, research design, and model validation.
Help
Help develop analytics culture through mentorship and training.
Help lead and set up an R& D team looking at emerging technologies and evolution of AR / VR.
Identify
Identify and interpret trends and patterns in datasets to locate influences.
Identify additive data requirements and help source new datasets.
Identify, assess and prioritize the Analytics & AI Modeling needs of partners across the enterprise.
Identify opportunities to enrich and integrate data from multiple, diverse sources.
Identify rich data sources and monitors the melding and cleaning of datasets to ensure consistency.
Identify trends and patterns to inform business decisions and product design.
Implement
Implement algorithms to power user-facing data-driven features.
Implement approaches and communicate insights in a compelling narrative.
Industrialize
Industrialize the processing of terabyte sized datasets for model training and inference.
Interface
Interface with business and customer teams to define, and track goals.
Interpret
Interpret and analyze data problems.
Interpret data and analyze results.
Keep up with
Keep up with the current state-of-the-art in machine learning.
Lead
Lead AI Express customer engagements from a data science perspective.
Lead and drive in deploy and testing of the solutions and insights.
Lead the execution of large scale, more complex analytics projects.
Leverage
Leverage Python to analyze complex data.
Linear
Linear and logistic regression.
Maintain
Maintain a positive, results orientated work environment.
Make
Make recommendations regarding the collection, extraction, and transformation of large data sets.
Make sense of complex data and translate that data into meaningful analysis.
Manage
Manage projects and lead a sub-portfolio of data science project teams to deliver results.
Manage the research and development process to improve R& D planning.
Meet
Meet and collaborate with business users on requirements, objectives and metrics.
Mentor
Mentor and develops junior data scientists and analysts.
Mentor complex projects using wide breadth of data sciences and advanced techniques.
Mentor newly hired Data Coordinators and junior Leads.
Mentor other team members / analysts to grow their skills and careers.
Mentor the team of data scientist and analysts.
Oversee
Oversee QA / QC of data and output to ensure accuracy, completeness and reliability.
Own
Own and deliver projects of diverse scope.
Participate in
Participate and lead data quality and cleaning initiatives.
Participate in and initiate defining new business opportunities and products.
Participate in project reviews and demos.
Perform
Perform code reviews to guarantee high quality products moving to production.
Plan
Plan and coordinate work within your team.
Plan and manage analytics projects, presentations and status communications to stake holders.
Plan model operationalization and rollout of solutions to business users.
Present
Present findings to team lead / managers and to external stakeholders.
Present to Senior Management & Leadership.
Produce
Produce quantitative and qualitative modeling of business dynamics, user behavior, etc..
Produce statistical tests and summarize test outputs.
Promote
Promote best practices within the team.
Provide
Provide deep knowledge and leadership in NLP / NLU statistical analysis as a thought leader.
Provide guidance and mentorship to junior team members.
Provide guidance and support to Data Scientists within your team.
Provide input into strategy, analysis methods, and tool selection.
Provide rationale for design choices.
Provide recommendation on how to move forward.
Provide research on topics including Hello Alice's business model and recommendation techniques.
Provide statistical consultation services.
Provide support for technical guidance and training of other team members.
Provide support to stakeholders in understanding analytics, models and test results.
Put
Put success of team above own interests.
Read
Read plans and specifications.
Recommend
Recommend implementable solutions to (or in collaboration with) business partners.
Recruit
Recruit and coach other data scientists.
Refine
Refine data collection and storage procedures.
Research
Research alternative techniques and technologies.
Research and analyze data to find deep relations within data.
Research best way to leverage new and existing technologies in current modeling pipelines.
Return
Return to work and our new normal.
Scale
Scale and share technical assets.
Seek
Seek feedback and acts upon it.
Set
Set strategy and work closely with founders.
Share
Share knowledge with other team members pro-actively by creating / sharing works in forums.
Spot
Spot and evaluate emerging / cutting edge, open source, data science / machine learning libraries.
Supervise
Supervise modeling work of Associate Data Scientists and Data Scientists.
Support
Support deployment and maintenance of data pipelines and models in a production environment.
Support more junior data scientists with Change Management.
Take
Take advantage of available resources to advance their technical and business skills.
Test
Test and improve components of Kettle's machine learning wildfire models.
Test performance of data-driven products.
Test performance of data driven solutions.
Track
Track CRFs as they are processed through the Data Management department.
Track record of creating actionable insights that drive business impact.
Track Record of publishing in peer reviewed academic literature.
Translate
Translate ambiguous statements into structured problem statements and testable hypotheses.
Translate analytical insights into clear recommendations.
Understand
Understand business problems to implement scalable and sustainable solutions.
Understand, communicate, and manage the end-to-end lifecycle of analytics on a given project.
Understand our Operating Principles.
Understand the corporate climate and culture.
Utilize
Utilize statistical approaches to build predictive models.
Visualize
Visualize data and create reports.
Work
Work closely with Engineering team to productionize winning approaches.
Work closely with other tech teams and Product Managers.
Work daily with Data Scientists, Data Engineers, DevOps and Fullstack Engineers.
Work well in interdisciplinary teams.
Work with a variety of business stakeholders to identify and prioritize use cases.
Work with cool people and impact millions of daily players.
Work with industry experts to deliver ad-hoc projects and product prototypes.
Work with senior leaders from all functions to explore opportunities for using advance analytics.
Work with Senior Management to create Road map and understand goals.
Work with the business teams and customers to continuously improve those models.
Work with the data architect teams on Google Cloud Platform / Amazon AWS / Azure.
Work with the development team to incorporate data model-driven intelligence into the application.
Write
Write clear and concise documentation of processing approaches and results.
Write high-quality, maintainable, and portable code.
Write production code and communicate with the entire technical team to impact algorithm production.
Write reports articulating the analytics insights and their business impacts.
Most In-demand Hard Skills
The following list describes the most required technical skills of a Lead Data Scientist:
AWS
Azure
Hadoop
Pytorch
Artificial Intelligence
Economics
Java
Keras
Physics
Scikit-Learn
Algorithms
Pyspark
Most In-demand Soft Skills
The following list describes the most required soft skills of a Lead Data Scientist:
Written and oral communication skills
Problem-solving attitude
Self-starter
Analytical ability
Interpersonal skills
Organizational capacity
Presentation
Creative
Curious
Flexible
Work independently with little direction
