Data Scientist 25-26

Experience Level: Assistant Manager / Deputy Manager (2–7 years)

To drive data-backed decision-making within the Sales division by applying advanced analytics, demand forecasting, price elasticity modeling, and GenAI solutions.

This role aims to improve forecasting precision, assess the impact of pricing strategies, and uncover actionable insights from customer behavior to optimize sales performance.

Develop scalable models to forecast enquiries, bookings, retails, and wholesales.

Use historical sales data and market trends to support short- and long-term planning.

Analyze price elasticity across segments and regions.

Measure the performance of offers and promotions using causal models and A/B testing.

Leverage GenAI and predictive modeling to segment customers, predict drop-offs, and inform engagement strategies.

Track and interpret patterns across the customer journey—conversion, preferences, loyalty.

Clean and integrate data across CRM, DMS, finance, and third-party systems.

Create self-serve dashboards and reports using Tableau, Power BI, or Salesforce Einstein.

Partner with Sales, Marketing, Finance, Product Planning, and Network teams to align insights with business strategy.

Forecasting in a dynamic, seasonal, and regionally diverse market.

Quantifying ROI from offers and pricing strategies.

Integrating financial KPIs into sales models.

Driving GenAI adoption in a traditional automotive ecosystem.

Creating insights from unstructured, siloed data across dealer networks.

Select and implement analytical models independently.

Recommend pricing structures based on customer behavior and elasticity.

Build analytics tools to enable stakeholder-led decision-making.

Prioritize projects based on business relevance and feasibility.

Define data processing methods for recurring analytical pipelines.

Graduate or Postgraduate degree in Data Science, Statistics, Engineering, or related field.

Strong command of Python, statistical modeling, machine learning, and data visualization tools.

Familiarity with auto industry sales metrics and business drivers.

Demonstrated experience in working with cross-functional teams.

Statistical Analysis, Machine Learning, Data Modeling, Python Programming, Dashboarding, Auto Industry Trends.

Effective Communication, Stakeholder Management, Creative Problem Solving, Flexibility & Continuous Learning.