Key Skills and Certification
KEY SKILLS & CERTIFICATION
Key modelling skills includes Deep learning using Keras, CNN, RNN, Encoder Decoder, Transfer learning, NMT, DL based NLP,
MLOps using Amazon Sagemaker Studio vs ML flow
Machine learning using linear regression, logistic regression, clustering, decision trees, factorizations methods like non negative
factorization and factor analyser, and data mining algorithms like apriori, FP growth (frequent pattern) mining for large datasets,
descriptive statistics, inferential statistics, and hypothesis framework.
Key Domain- Retail / CPG, Ecommerce, Consumer Facing Industries
Key Customer Analytics frameworks in areas of customer analytics like loyalty, lifestyles, category planning, price & promotions,
Personalization
Analytical tools: Current focus is on Graph based ML vs traditional ML and Pyspark. R, SAS are used in last organisations, Tableau,
Power BI, Customer Data Platforms like Sitecore CDP, Experience and Personalization
CORE COMPETENCIES
- Data Science & Analytics Project Execution & Management
- Automation / Advance Analytics Statistics Projects
- Forecasting & Prediction / Decision Analytics
- Machine Learning / Algorithms /
- Artificial Intelligence
- Requirement Gathering & Analysis /
- Data-driven Personalization
- Data Cleansing/Mining/
- Visualization/Exploration
- Client/Stakeholder Management
Manager, Data Science
Key KPIs: Responsible and accountable for managing EUCIS portfolios. Ensure that key risk metrics like approval rate, decline rate, fraud rate etc. are optimal and stable.
• Fraud Risk Models and Strategies: Build Fraud risk models and strategies to optimise the risk metrics Team Development and management: Managing a team of 5 direct reportees. Actively involved in hiring and developing a high performance team from scratch.
• Cross Team Collaborations: Works closely with various teams such as Business, marketing, Engineering, Product, Fraud Investigations and monitoring, Model and Rules deployment, Compliance, Payments Operations etc.
• Stakeholder Management: Manages various stakeholders (both internal and external), addresses escalations/concerns
Company : XYZ
LEAD Data Scientist – Data Science
- Analytics Lead for implementing the Multi-Channel Promotion Orchestration Solution for one of the new and upcoming brands for increasing Customer engagement thereby leading to increased sales for the Brand
- Lead Data Scientist for designing the Hyper-Personalization Analytics Solution for implementing the Brand Content Strategy and also as an input to the Multi-Channel Orchestration Solution
- Building and Productionization of the Multi-Channel Orchestration Solution which includes the Decision Engine, Optimizer for converting the model outputs to actionable Customer, Channel and Content Level Tactics
- Create the Implementation Roadmap for the Multi-Channel Orchestration Recommendations into the field
- Designing the Test/Control and A/B Testing Strategies for various Brands Build Early Adopter, Breadth and Depth Analysis and Customer Potential Models
- Engaging with the Brand Teams to identify the important Business Problems and provide an Analytics Solution and Roadmap for solving the same
- Built a supply planning tool (SPT) using SHAP & tree based regression to attribute impact of supply on booking completion
- Built hierarchical version of SPT to help business plan supply in small areas like subdistrict, district and service area
- Built an image classification tool to label KYC documents as lowlight, highlight and blur on mobile app
