WHAT DOES A DATA SCIENTIST DO?

Overview Job Description Resume Cover Letter Skills Roles

Published: Jun 04, 2025 - The Data Scientist builds and deploys end-to-end AI products using statistical, machine learning, and deep learning models to solve real-world business problems. This position extracts actionable insights through quantitative research and presents findings with clear visualizations to support strategic decision-making. This role enhances business efficiency by automating data processes, optimizing workflows, and identifying new product opportunities through advanced data analysis.

A Review of Professional Skills and Functions for Data Scientist

1. Data Scientist Duties

Machine Learning: Architect, design, and develop advanced, robust, scalable, applied machine learning (cutting-edge) algorithms for a variety of business applications.
Algorithm Optimization: Optimize, fine-tune, and improve the scalability, design, and robustness of existing ML algorithms and techniques.
Team Collaboration: Partner and collaborate with a larger data science team including Data Scientists, Data Engineers, and Analysts, to optimize business performance through improving ML algorithms.
Applied Research: Conduct applied research on robust machine learning, run large-scale experiments to investigate the robustness of different approaches for large data sets.
Distributed Systems: Develop distributed ML algorithms and infrastructure to support training on a variety of different backends.
Research Awareness: Stay up to date on advanced ML/AI/DL research, ideas, and software.
Stakeholder Liaison: Liaise with internal and external stakeholders to design surveys fitting customer needs, advise on the most appropriate methods to obtain desired insights for sector insights, customer feedback, and marketing.
Product Design: Design and develop new survey products and services as part of a small, agile data science team.
Data Insights: Derive new and valuable insights from data and communicate these effectively to decision makers.

2. Data Scientist Details

Machine Learning: Advise on and help implement models in Machine Learning, Optimization, Neural Networks, and Artificial Intelligence such as Natural Language and other quantitative approaches.
Client Engagement: Act as a key contributor to a pre-sales Garage team, partnering with clients to understand business problems and propose solutions.
Solution Development: Contribute to the co-creation of rapid proofs of concept and minimally viable solutions that demonstrate business value, leading to client investment in strategic solutions.
Business Analytics: Translate business problems into leading-edge analytics solutions using consulting skills, industry expertise, and technical knowledge.
Trend Prediction: Deliver meaningful insights and predict emerging trends to inform business solutions that optimize client value.
Methodology Research: Research and develop new methodologies for demand forecasting and price modeling.
Model Enhancement: Improve upon existing methodologies by adding new data sources and implementing model enhancements.
Performance Tracking: Create and track accuracy and performance metrics (both technical and business metrics).
Documentation Management: Create, enhance, and maintain technical documentation, and present to other scientists, engineers, and business leaders.
Team Leadership: Drive best practices on the team and mentor and guide junior members to achieve career growth potential.

3. Data Scientist Responsibilities

Collaboration Skills: Actively collaborate with technical and non-technical business unit and marketing peers to solve data science problems for the business
Industry Knowledge: Understand industry standards, assumptions, methodologies, technologies, and current data science practices
Analysis Delivery: Deliver on-time analysis, interpretation, and actionable recommendations that enable intelligent decisioning that creates value for the company
Model Lifecycle: Understand and apply the model development lifecycle from framing, data collection, through development, deployment, and performance measurement
Data Systems: Understand and contribute to the design and development of ML-ready data systems and processes
Project Requirements: Assist in articulating the unique and iterative requirements of analytics project development, specifically big data sourcing, ETL, and feature engineering unique to advanced modeling and machine learning solutions in a matrixed corporate environment
ML Implementation: Leverage Cisco’s data to design, implement, and deploy Machine Learning technologies into reliable and scalable services independently and in a team setting
Project Management: Drive end-to-end projects by identifying, capturing, cleansing, verifying, analyzing, and presenting data associated with key business problems while utilizing state-of-the-art methods and tools
Problem Solving: Answer sophisticated business questions by internalizing business problems, applying structured problem solving, performing technical analyses, drawing inferences, and delivering impactful insights and recommendations
Stakeholder Engagement: Engage with business stakeholders and manage/cultivate long-term projects, build technical and non-technical plans, processes, and metrics for achieving success
Model Supervision: Supervise and ensure lifecycle maintenance of Machine Learning models and solutions, focusing on quality and impact
Technical Planning: Develop or contribute to a technical roadmap or project planning

4. Data Scientist Job Summary

Data Mining: Use data mining, model building, and other analytical techniques to develop and maintain customer segmentation and predictive models to drive the business and improve marketing performance.
Quantitative Leadership: Provide leadership and creativity by utilizing advanced quantitative methods from statistics, operations research, economics, and data mining to identify opportunities that bring value and influence decision making.
Analytical Problem-Solving: Apply strong analytical skills and efficiently frame and solve unstructured and complex analytical problems including response and uplift models, cross-sell/up-sell analysis, retention models, churn forecasting, offer optimization, and customer value analysis.
Consulting Expertise: Act as an analytic consultant and project manager to provide actionable business solutions.
Model Development: Create advanced analytics models using statistical and machine learning methods.
Product Collaboration: Work with software engineering and product teams to create intelligent products using machine learning and AI.
Client Interaction: Interact with clients in various domains who have a spectrum of complex problems.
Data Communication: Use data visualizations and storytelling to communicate effectively.
Agile Delivery: Apply a Lean/Agile delivery process to the evolutionary creation of value from data.
Community Representation: Represent yourself and the Data community in various online and offline forums (events, conferences).
Career Development: Develop a career outside traditional paths by focusing on passions rather than a predetermined plan.

5. Data Scientist Functions

AI Development: Building and launching end-to-end AI products.
Model Building: Developing statistical, machine learning, and deep learning models to solve practical business problems and deploying the models to production.
Quantitative Research: Conducting quantitative research through statistical analysis and developing actionable insights.
Insight Presentation: Presenting the insights clearly to stakeholders.
Stakeholder Collaboration: Collaborating with stakeholders in other departments to execute strategic initiatives through project-based research and ad-hoc analysis.
Process Automation: Driving efficiencies in business with automation of data and information, and identifying opportunities where existing data can provide enhanced business benefits.
Technical Communication: Translating technical findings to non-technical audiences, using well-designed visualizations with tools such as Tableau.
Product Research: Research new data sets and use technical expertise to recommend new product offerings and generate premium content.
Programming Skills: Using scripting and programming to report project progress, create data visualizations, and propose new analysis methods.
Process Analysis: Analyzing metadata and current processes to find improvement opportunities.
Data Validation: Reviewing market conventions and data relationships to set rules for data validation.
Project Leadership: Optimizing processes, leading projects, and improving product quality for internal and external end-users.

6. Data Scientist Job Description

Data Analysis: Analyze large sets of transactional data to understand consumer behavior, explore and extract features and patterns to improve model performance.
ML Research: Research state-of-the-art machine learning technologies to build world-class fraud detection models.
Model Prototyping: Prototype modeling strategies to optimize model performance.
Fraud Analytics: Perform link analysis and fraud analytics in an enterprise environment.
Domain Knowledge: Acquire and apply knowledge relevant to consumer behavior, risk management, and payment processing.
Policy Collaboration: Work with interdepartmental teams to maintain and improve risk policies and procedures.
Issue Resolution: Identify possible problems with data or processes and take action to resolve issues.
Model Development: Conduct proof of concepts and develop production models to achieve Vesta's strategic and operational objectives.
Solution Building: Build data-centered solutions empowering data-driven decision-making, streamlining processes, improving targeting, and predicting outcomes.
Tech Stack: Leverage the data science toolkit, including Python, R, SQL, Spark, React, and Tableau.
Stakeholder Engagement: Work closely with stakeholders to build and iterate on solutions and analyses to accelerate business impact.
Communication Skills: Collaborate and communicate clearly with technical and non-technical stakeholders to transform vague ideas into verified solutions.

7. Data Scientist Overview

Translational Medicine: Developing and implementing novel approaches in translational medicine.
Data Integration: Applying analytic and interpretive methods to integrate a wide variety of health and genomic data to improve treatment and prevention.
ML Pipelines: Build machine learning and deep learning pipelines to assess risk for diseases using integrated big data from various sources including genetics, genomics, EMRs, social, behavioral, environmental, wearable, and imaging data.
Data Standardization: Standardize and normalize data extracted from electronic medical records using Common Data Models such as OMOP, RxNorm, LOINC, and CCS.
Drug Insights: Identify novel indications or side effects for drugs prescribed to patients to recommend strategies improving the standard of care.
Business Collaboration: Work with the Business Development team, collaborating with pharmaceutical and insurance partners to use aggregate patient data to assess clinical questions.
Business Understanding: Understand business problems within Data Science teams.
Data Transformation: Identify, explore, and transform data for Data Science tasks.
Model Building: Research, design, and build models to solve key business problems.
Model Validation: Test and validate model performance.
Solution Presentation: Present solutions to data science teams and internal customers.

8. Data Scientist Tasks

Stakeholder Management: Work with the executive team and other cross-functional stakeholders such as Product, Marketing, Partnership, and Compliance.
Performance Analysis: Instrument measurement tools, track, report, and analyze marketing and sales performance.
Product Development: Assist in the development, maintenance, and enhancements of Customer Data Products.
Insight Delivery: Deliver insights and recommendations to drive efficiency across the company.
Data Transformation: Perform data transformation (ETL), build and maintain relational databases.
Report Development: Develop business performance reports as part of Customer Data Products deliverables.
Quantitative Analysis: Apply expertise in quantitative analysis, data mining, and data presentation to make product, marketing, and forecasting decisions.
Team Collaboration: Partner with Product, Partnerships, and Engineering teams to use data for insightful decisions.
Dashboard Building: Build dashboards and reports, monitor key data product metrics, and understand root causes of changes in metrics.
Taxonomy Definition: Define taxonomy and build instrumentation for product analytics and marketing analytics.

9. Data Scientist Roles

Statistical Modeling: Apply statistical analysis and modeling techniques with finance intuition to datasets, large and small.
Research Innovation: Advance existing initiatives and pursue new and previously unexplored research topics across various industries and domains.
Metric Forecasting: Build, forecast, and report on metrics that drive strategy and facilitate decision making for key business initiatives.
Experiment Design: Design and analyze A/B experiments to evaluate the impact of changes made to the product and business.
Data Exploration: Explore vast data sets to identify and investigate new opportunities and efficiencies.
Feature Engineering: Visualize and explore data sets to enable ideation and generation of new predictive features.
Data Integration: Work with teams to support data collection, integration, and retention requirements, incorporating business knowledge and best practices.
Performance Monitoring: Provide ongoing tracking and monitoring of performance decision systems and statistical models.
Requirement Translation: Translate business requirements into system requirements.
Strategy Development: Participate in creating strategies that use business intelligence and data platforms.
Machine Learning: Apply machine learning techniques and algorithms.

10. Data Scientist Additional Details

Model Development: Responsible for designing, developing, and testing new models or solving approaches under the supervision of a senior team member or manager.
Supervised Execution: Work assignments are performed under supervision with specific procedures and guidelines to follow.
Task Delivery: Deliver all tasks on time and with quality following provided guidance.
System Maintenance: Keep all applicable systems updated based on standards consistently.
Solution Application: Demonstrate the ability to apply an approach to a solution from research to a BlueYonder business problem.
Algorithm Design: Develop algorithms and models based on machine learning, operations research, or other techniques to solve a BlueYonder business problem.
Model Integration: Integrate new models and algorithms into product solutions under supervision.
Data Analysis: Proactively find patterns and anomalies in the data to seize opportunities and avoid catastrophes.
Cross-team Collaboration: Work with designers across teams to answer questions and suggest features or changes to improve product performance.
Experiment Design: Design, implement, and communicate results of A/B tests to improve the product.

Editorial Process and Content Quality

This content is part of Lamwork's career intelligence platform and is developed using structured analysis of real-world job data, including publicly available job descriptions, skill requirements, and hiring patterns.

Lam Nguyen, Founder & Editorial Lead, defines the research framework behind Lamwork's career intelligence platform, including job role analysis, skills taxonomy, and structured career insights.

All content is reviewed by Thanh Huyen, Managing Editor, who oversees editorial quality, content consistency, and alignment with real-world role expectations and Lamwork's editorial standards.

Content is developed through a structured process that includes data analysis, role and skill mapping, standardized content formatting, editorial review, and periodic updates.

Content is reviewed and updated periodically to reflect changes in skills, role requirements, and labor market trends.