WHAT DOES A DATA SCIENTIST DO?
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.