Lead - Data Science

Role & responsibilities

Key responsibilities:

Analytics & Data Science

• Strategy Formulation & Execution

  : Participate in the conceptualization of AI & Data Science strategy, develop, execute and sustain; strive to build a practice and organization culture around same

• Manage & Enrich Big Data

  Work on Datawarehouse for sales, consumer data, manufacturing data and all the user attributes, think of ways to enrich data (both structured/unstructured)
• Engage with Data Engineering team on building/maintaining/enhancing Data Lake/Warehouse in MS Azure Databricks (Cloud)
• Data preparation, new attribute development, preparing Single View of consumers, Single View of Retailers/electricians etc

• Consumer Insights

   &

  Campaign Analysis

   - Drive adhoc analysis & regular insights from data to generate insights and drive campaign performance
• Build a Gen AI powered Consumer Insights Factory
• Support insights from consumer, loyalty, app, sales, transactional data
• Data mining/AI/ML to support upsell/cross-sell/retention/loyalty/engagement campaigns & target audience identification
• Purchase behavior/market basket analysis

• Predictive Analytics & Advanced Data Science

   - Build & maintain Predictive analytics AI/ML models for use cases in Consumer Domain, Consumer Experience (CX), Service, example:
• Product recommendations
• Likely to buy product or services (AMC)
• Lead scoring conversion
• Service Risk Scoring or Service Franchise/Technician performance score
• Likely to be a detractor or Likely to churn
• Market mix modelling

• Dashboarding:

  Build, manage, support various MIS/dashboards via Data Engineering/Visualization team
• Power BI dashboards & other visualization
• Adhoc dashboards

Analytics & Data Science Other Domains SCM, Sales Op, Manufacturing, Marketing

Support AI/ML models for Sales Transformation or SCM or Marketing Use Cases:

• Market Mix Modeling
• Retailer/Electrician loyalty program optimization
• Retailer/partner risk scoring or churn prediction
• Product placement & channel partner classification
• Improve forecast accuracy & Out of Stock prediction

Gen AI Use Cases

• Deep data mining to support digital analytics, website behavior, app behavior analytics, call center/CS behavior, NPS, retailer & electrician loyalty etc

Preferred candidate profile

7-14 years of direct experience in predictive analytics, decision science and AI in atleast two domains out of the following: consumer, sales operations, Supply chain, manufacturing, in any industry. Hands-on experience & knowledge of modern analytical tools, techniques & software (python, R, SQL, SPSS, SAS). Experience of building & leading team is a must.