Senior Data Scientist
Primary Skills
Problem Formulation (Business problem to Data Science Problem), OKR Validation against statistical measures, Data Wrangling, Data Storytelling & Insight Generation, Problem Solving, Excel VBA, Data Curiosity, Technical Decision Making (How many iterations to go for vs when to stop iterating), Communication & Articulation: Vocal & Written, Business Acumen (Consume new domains quickly to learn through data), Design Thinking, Data Literacy Specialization
Data Science Foundation: Data Scientist Job requirements
Experience Range: With at least 5 years of experience, including up to 8 years designing and delivering data-driven solutions in decision analytics and business intelligence Key Responsibilities: 1. Lead end-to-end development of scalable data science and recommendation solutions, translating business requirements into actionable customer insights 2. Develop statistical and machine-learning models for customer targeting, cross-sell/upsell, acquisition, retention, and churn management 3. Build and implement recommendation frameworks utilizing customer segmentation, product usage signals, revenue and opportunity data, and business rules 4. Conduct advanced analytics including propensity modeling, opportunity sizing, quantile regression, and peer benchmarking to identify growth potential 5. Own the analytics lifecycle from stakeholder requirement gathering, data exploration, feature engineering, model development, validation, testing, and production deployment 6. Integrate analytical solutions with business intelligence platforms, designing KPIs, dashboards, and data visualizations to drive measurable business outcomes 7. Collaborate with business and leadership teams to validate solutions, define business rules, and communicate insights to executive stakeholders 8. Provide technical leadership through solution design, documentation, code reviews, knowledge sharing, and mentoring junior team members Required Skills: 1. Python 2. Pandas 3. PySpark 4. SQL 5. Databricks 6. Statistical modeling (propensity modeling, customer segmentation, quantile regression) 7. Feature engineering 8. Predictive analytics 9. Power BI 10. Git 11. Azure DevOps 12. Analytics pipeline production validation and deployment Preferred Skills: 1. Excel VBA 2. Analytics pipeline automation and orchestration tools 3. Peer benchmarking methodologies 4. Experience with solution documentation and user guide creation Desired Qualifications: 1. Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Information Technology, or a closely related discipline 2. Microsoft Certified: Azure Data Scientist Associate or IBM Data Science Professional Certificate 3. Power BI Data Analyst Associate certification Additional Information: Business focus: Deliver analytics that help business and sales teams identify which customers to target, which products or services to recommend, why an opportunity exists, how large the opportunity could be, and what action should be taken, ultimately supporting revenue growth, portfolio penetration, customer retention, and better sales prioritization.
