New opportunity

Lead Data Scientist

Sensata Technologies · Pune, India

About this role

The Lead Data Scientist (Focus: Commercial) serves as the primary data science partner to Sensata's commercial organization, including Marketing, Sales, Revenue Operations, and Commercial Finance. This role is responsible for building machine learning, forecasting, and applied optimization models to improve pricing, sales strategies, customer prioritization, and performance forecasting. Responsibilities: Oversee the development of innovative product solutions and lead project teams by defining data needs. Analyze large datasets to discover trends and insights, develop predictive models and algorithms, and establish standards for model validation, testing, and monitoring. Partner with AI Engineers and platform teams to deploy models into production and integrate them into business workflows. Evaluate third-party analytics and AI solutions to support technology decisions. Deliver ad hoc analyses for enterprise priorities. Qualifications: Requires 7+ years of experience in data science, machine learning, or applied statistics, ideally supporting commercial teams. Strong expertise in classification, propensity modeling, time-series forecasting, regression, causal analysis, and experimentation is required. Working knowledge of applied optimization methods (e.g., linear or mixed-integer programming) is essential. Proficiency in Python and SQL is required, with experience in Azure ML or Databricks preferred. A university degree is required; an advanced degree in Data Science, Statistics, Economics, Applied Mathematics, Computer Science, or a related quantitative field is preferred. Candidates must possess strong communication and interpersonal skills to explain complex models to non-technical stakeholders and drive adoption.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Data ScienceMachine LearningApplied StatisticsClassificationPropensity ModelingTime-series ForecastingRegressionCausal AnalysisExperimentationApplied OptimizationLinear ProgrammingMixed-integer ProgrammingPythonSQLAzure MLDatabricksCRMSalesforceERPData Visualization

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Commercial Impact and Strategy
The role acts as the primary data science partner for Marketing, Sales, and Finance. You must prioritize translating high-level business questions—such as pricing elasticity, churn, and lead scoring—into actionable, well-scoped data science problems that directly influence commercial outcomes.
Model Lifecycle and Productionization
Beyond model development, you are responsible for the end-to-end lifecycle, including testing, backtesting, and monitoring. You must partner with AI Engineers and platform teams to ensure models are successfully deployed into production and integrated into existing business workflows like CRM and ERP systems.
Applied Optimization and Forecasting
A core requirement is the application of advanced quantitative methods, specifically linear or mixed-integer programming for resource allocation and pricing, alongside time-series forecasting for revenue and pipeline performance.
Stakeholder Communication and Adoption
You must bridge the gap between complex technical models and non-technical executive stakeholders. Success depends on your ability to explain findings in decision-ready terms to drive organizational adoption of data-driven strategies.

Likely challenges to think through

Translating Ambiguous Commercial Needs
Commercial stakeholders may present vague business problems. Prepare by practicing how you would interview business leaders to extract specific requirements, define success metrics, and scope a project that balances technical rigor with immediate business utility.
Integrating Models into Legacy Workflows
Deploying models into established CRM or ERP systems often faces resistance or technical friction. Prepare by discussing your experience in change management and how you have previously navigated the trade-offs between model complexity and the ease of integration into existing business processes.
Balancing Ad-hoc Requests with Long-term Projects
The role requires handling both long-term model development and urgent ad-hoc enterprise analyses. Prepare by demonstrating your time management strategies and how you prioritize competing demands without sacrificing the quality or validation standards of your primary projects.

Practice your answers

Technical practice

Applying Optimization to Commercial Pricing

What to coverExplain how you would approach a margin optimization problem using linear or mixed-integer programming. Discuss the trade-offs between model complexity and computational feasibility when dealing with large-scale product and customer-level pricing data.

Time-Series Forecasting for Revenue and Pipeline

What to coverDescribe your methodology for building forecasting models that combine internal commercial data with external market indicators. Address how you handle seasonality, trend shifts, and the integration of external peer data to improve accuracy.

Propensity Modeling and Customer Churn

What to coverDiscuss the process of developing a propensity model for churn or next-best-offer strategies. Focus on feature engineering from CRM and ERP data, and how you validate the model's predictive power before deployment.

Causal Analysis in Experimentation

What to coverHow do you design and interpret experiments to determine the causal impact of a new sales strategy or pricing change? Explain how you isolate variables to ensure the observed results are statistically significant and actionable.

Model Validation and Production Lifecycle

What to coverDetail your standards for model validation, including backtesting and monitoring for drift. Describe how you collaborate with AI Engineers to ensure models remain accurate and performant once embedded in business workflows.

Evaluating Third-Party AI Solutions

What to coverWhen tasked with evaluating a third-party analytics tool, what criteria do you use to validate their performance claims? Suggest a framework for assessing whether a vendor solution aligns with enterprise needs versus building a custom model.

Handling Large-Scale Commercial Datasets

What to coverDescribe your experience using SQL and Python to extract and transform data from complex systems like Salesforce or ERPs. Discuss strategies for managing data quality and consistency when merging disparate sources for predictive modeling.

Regression and Classification Techniques

What to coverCompare the use of regression versus classification models in the context of lead and opportunity scoring. Explain how you select the appropriate algorithm based on the business objective and the nature of the available data.

Behavioural practice

Translating Technical Insights for Non-Technical Stakeholders

What to coverProvide an example of a time you had to explain a complex model to an executive or non-technical stakeholder. Describe how you adapted your communication to ensure they understood the business value and felt confident in adopting the model's recommendations.

Managing Competing Priorities in a Fast-Paced Environment

What to coverDescribe a situation where you had to balance multiple competing data science projects or ad hoc requests. How did you prioritize your tasks, and what steps did you take to ensure you met the needs of different commercial stakeholders?

Collaborating with Cross-Functional Teams

What to coverGive an example of a successful collaboration with AI Engineers or platform teams to deploy a model into production. How did you handle technical disagreements or integration challenges to ensure the project's success?

Driving Adoption of Data-Driven Strategies

What to coverTell me about a time you identified a business challenge and proposed a data science solution that changed how a team operated. What was the outcome, and how did you overcome any initial resistance to the new process?

Sharpen your knowledge

Topics to prepare

Applied Optimization for Commercial Decisions
The role requires using linear or mixed-integer programming for pricing, resource allocation, and scheduling. Exercise: Review the formulation of a classic product-mix or price-optimization problem using Python libraries like PuLP or SciPy. This is relevant because the role explicitly lists these methods for improving commercial performance.
Model Lifecycle and Validation Standards
The position involves setting standards for testing, backtesting, monitoring, and retraining models. Exercise: Draft a framework for a model monitoring dashboard that tracks data drift and performance degradation for a churn prediction model. This is relevant as the role requires ensuring models remain accurate after deployment into production workflows.
Causal Analysis and Experimentation
The role requires expertise in causal analysis and experimentation to support sales and marketing strategies. Exercise: Practice designing an A/B test for a pricing change and identifying potential confounding variables that could bias the results. This is relevant because the role focuses on driving adoption and providing decision-ready insights to commercial stakeholders.

Skills to revise

Python for Commercial Modeling
Proficiency in Python is required for building predictive and optimization models. Exercise: Build a pipeline that cleans a sample CRM dataset and trains a classification model to predict customer propensity. This is relevant as the role involves integrating models into business workflows and partnering with AI engineers.
SQL for Data Extraction and Analysis
Strong SQL skills are needed to analyze large datasets from CRM and ERP systems. Exercise: Write complex queries involving window functions and joins to aggregate sales performance metrics across different product lines. This is relevant because the role requires analyzing large datasets to discover trends and insights for the commercial organization.
Time-Series Forecasting
The role involves forecasting revenue, bookings, and pipeline performance. Exercise: Develop a time-series model using historical data to forecast future demand, incorporating external market indicators. This is relevant as the role specifically tasks the lead with building forecasting models for market and revenue performance.

Your preparation checklist

Prepare a commercial data science project walkthrough
Select a past project where you applied machine learning to commercial challenges like lead scoring, churn prediction, or pricing optimization. Be ready to explain the business problem, the specific algorithms used (e.g., classification, regression), and how you measured the model's impact on business KPIs.
Develop a case study on applied optimization
Prepare a brief explanation of how you have utilized linear or mixed-integer programming to solve resource allocation or scheduling problems. Focus on how you translated business constraints into mathematical models and the resulting operational improvements.
Refine your communication of complex models
Practice explaining a technical model (such as a time-series forecast or causal analysis) to a non-technical stakeholder. Focus on translating technical metrics into decision-ready insights that address specific business needs in sales, marketing, or finance.
Review your experience with commercial data ecosystems
Prepare to discuss your proficiency in SQL and Python for handling large datasets from CRM (e.g., Salesforce) and ERP systems. Highlight your experience in deploying models into production environments, specifically mentioning any familiarity with Azure ML or Databricks.
Formulate strategic questions for the interviewers
Prepare questions regarding the current maturity of Sensata's commercial data infrastructure, the typical collaboration process between data scientists and AI engineers, and how the team prioritizes ad-hoc enterprise requests against long-term model development.

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