IN_Senior Associate_Data modelling_ Data and Analytics_Advisory_Pan India

Responsibilities 

• Design and implement scalable forecasting and predictive analytics models to solve complex business challenges across multiple domains. 

• Develop and optimize data models (logical, physical, dimensional, and semantic) to support analytics, ML, and reporting use cases. 

• Work with large, complex datasets from multiple sources—cleansing, transforming, and preparing them for analytical consumption. 

• Build, train, and evaluate ML models using statistical and machine learning techniques to enhance accuracy, performance, and interpretability. 

• Collaborate with data engineers and cloud teams to integrate ML pipelines into AWS or Azure environments using modern ETL and orchestration tools. 

• Translate business objectives into technical data solutions and actionable insights through strong analytical reasoning and stakeholder communication. 

• Ensure data quality, lineage, and consistency through standardized data definitions, metadata documentation, and model versioning practices. 

• Continuously improve models through retraining, drift detection, and performance monitoring using MLOps best practices. 

• Build SQL queries, tables & schemas for large datasets converting unstructured data into meaningful transformations for analysis. 

• Desing statistical tests, model assumptions, build EDA reports & POCs as and when required. 

Required Skills and Experience 

• Proven expertise in machine learning and statistical modeling for forecasting, demand prediction, or time-series analysis. 

• Strong data modeling skills across dimensional, relational, and semantic structures. 

• Advanced proficiency in Python for data wrangling, feature engineering, and ML model development (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow preferred). 

• Strong practical & theoretical knowledge of statistical concepts including data science models. 

• Strong SQL skills with experience writing efficient queries and optimizing database performance. 

• Strong analytical and problem-solving mindset with the ability to derive insights and communicate outcomes to technical and business stakeholders. 

Mandatory Skill sets: 

 Good exposure to retail and consumer 

Technical skills 

ML engineer with vast experience in solving prediction and forecasting with strong practical knowledge of all traditional machine learning models & deep learning models. 

Data Modelling is a must (building relational data & table schema) 

Strong knowledge of theoretical & practical concepts of statistics including logical & statistical understanding of ML models. 

ETL/Data Engineering skills on AWS/Azure is good to have. 

Advance level SQL skills. 

Python skills excellent. 

Knowledge of GenAI & Agentic AI is good to have. 

Knowledge of MLOps intermediate level.  

Preferred Skill sets: 

• Domain experience in retail, supply chain, demand forecasting, or CPG analytics. 

• Strong interest in emerging areas such as Generative AI or AI-driven forecasting automation. 

• Exposure to cloud ecosystems (AWS, Azure) including data engineering components like Glue, Data Factory, Lambda, Databricks, or Synapse.