Role Purpose
The role is responsible for applying actuarial and advanced quantitative techniques to transform historical sales data into predictive insights enabling business to forecast future sales revenue , plan incentive schemes, suggestions for new products for developent , optimize product pricing risk, optimize costs, improve pricing, warranty provisioning, inventory risk, and logistics efficiency within the automotive parts, accessories. The position supports data‑driven decision‑making across supply chain, aftersales, warranty.
Key Responsibilities
1. Predictive Sales Forecasting - Utilize historical data and statistical models to predict future sales volume and trends . by analyzing past trends , create models that forecast how various factors ( eg. economic shifts,new regulations, customer behavious changes ) will impact future . Suggest change in the incentive schemes based on shift in sales under various heads . 2. Pricing Optimization - Determine the optimal price for various products to balance profitabilitu with competitiveness . 3. Customer Segmentation - Segment customers into actionable groups based on potential profitability and risk . a. Identify customer segments ( eg young professionals, middle age high vehicle users , based on cities/towns , income levels etc) who may need specific products , allowing procurement and sales team to plan accordingly .Shifts in the technology and buying patterns which helps in develop products & change marketing campaigns . 3. Incentive Schemes - Design effective incentive schemes which gives the best Sales by modelling different incentive scenarios while staying within the company's budgeted figures . 4. Channel Inventory -Develop actuarial models to evaluate the inventory risks across parts and accessories supply chains.Reduce - obsolescence, demand volatility, and supplier disruptions.
3. Pricing & Profitability Analytics
Support pricing of parts, accessories, service packages, and logistics services. Analyze contribution margins by SKU, region, channel, and logistics mode. Conduct sensitivity and scenario analysis to guide pricing strategy under inflation, and demand uncertainty.
4. Data, Reporting & Governance: Translate complex actuarial outputs into clear business insights for functional heads. Create dashboards, reports, and KPIs for risk indicators, and performance tracking.
Ensure compliance with internal risk frameworks.
Education
Bachelor’s/Master’s degree in Actuarial Science, Mathematics, Statistics, Economics, Engineering, or related field
Actuarial qualification (student, associate, or fellow) from IAI / IFoA / SOA / CAS preferred
3–10 years of actuarial, analytics, or quantitative risk experience
Exposure to automotive, manufacturing, logistics, insurance, or supply chain analytics preferred
Experience working with large datasets and cross‑functional teams
Technical Skills
Strong command of statistics, probability, and actuarial modeling techniques
Proficiency in Excel, SQL, R/Python, Power BI/Tableau
Familiarity with supply chain metrics, pricing models, and risk frameworks
Behavioral & Leadership Competencies
Strong analytical and problem‑solving mindset
Ability to simplify complex data for business audiences
Stakeholder management and influencing skills
High attention to detail with business orientation
Key Success Metrics
Accuracy of cost and risk projections
Improvement in warranty profitability and reserve adequacy
Reduction in logistics and inventory risk exposure
Enhanced pricing effectiveness and margin optimization
Quality and timeliness of decision support to leadership
