Lead - Data Scientist

Lead - Data Scientist

Description:

Knowledge:

- Functional Analytics (Supply chain analytics, Marketing Analytics, Customer Analytics, etc.) experience in Statistical modelling using Analytical tools (R, Python, KNIME, etc.)

- Knowledge of statistics and experimental design (A/B testing, hypothesis testing, causal inference)

- Practical experience building scalable ML models, feature engineering, model evaluation metrics, and statistical inference.

- Practical experience deploying models using MLOps tools and practices (e.g., MLflow, DVC, Docker, etc.)

- Strong coding proficiency in Python (Pandas, Scikit-learn, PyTorch/TensorFlow, etc.)

- Big data technologies & framework (AWS, Azure, GCP, Hadoop, Spark, etc.)

- Enterprise reporting systems, relational (MySQL, Microsoft SQL Server etc.), non-relational (MongoDB, DynamoDB) database management systems and Data Engineering tools

- Business intelligence & reporting (Power BI, Tableau, Alteryx, etc.)

- Microsoft Office applications (MS Excel, etc.)

Roles & Responsibilities:

Analytics & Strategy:

1. Analyse large-scale structured and unstructured data; develop deep-dive analyses and machine learning models in retail, marketing, merchandising, and other areas of the business

2. Utilize data mining, statistical and machine learning techniques to derive business value from store, product, operations, financial, and customer transactional data

3. Apply multiple algorithms or architectures and recommend the best model with in-depth description to evangelize data-driven business decisions

4. Utilize cloud setup to extract processed data for statistical modelling and big data analysis, and visualization tools to represent large sets of time series/cross-sectional data

Operational Excellence:

- Follow industry standards in coding solutions and follow programming life cycle to ensure standard practices across the project

- Structure hypothesis, build thoughtful analyses, develop underlying data models and bring clarity to previously undefined problems

- Partner with Data Engineering to build, design and maintain core data infrastructure, pipelines and data workflows to automate dashboards and analyses

Stakeholder Engagement:

- Working collaboratively across multiple sets of stakeholders Business functions, Data Engineers, Data Visualization experts to deliver on project deliverables

- Articulate complex data science models to business teams and present the insights in easily understandable and innovative formats