Associate Data Scientist

Key Responsibilities:

- Client & Stakeholder Engagement: Collaborate with business clients to understand pain points, translate requirements into technical specifications, and present data-driven recommendations clearly to non-technical audiences.

- Exploratory Data Analysis (EDA): Clean, preprocess, and analyse large structured and unstructured datasets to identify patterns, trends, and anomalies.

- Predictive & Prescriptive Modelling: Design, build, validate, and deploy ML models (e.g., classification, regression, clustering, time-series forecasting, NLP) to solve business challenges.

- Dashboarding & Storytelling: Create clear, impactful visualisations and executive-ready dashboards using BI tools to communicate findings effectively.

- Code Optimisation & Version Control: Write production-quality, reproducible Python/R code, maintaining version control best practices and participating in code reviews.

- Cross-Functional Collaboration: Partner closely with Data Engineers, Product Managers, and Solution Architects to integrate algorithms into client workflows or production pipelines.

Technical Expertise:

- Programming: Advanced proficiency in Python, including libraries like pandas, NumPy, scikit-learn, statsmodels, and matplotlib/seaborn.

- Database & SQL: Strong proficiency in writing complex SQL queries to extract, join, and manipulate data from relational databases (e.g., PostgreSQL, MySQL, Snowflake, BigQuery).

- Machine Learning: Solid background in supervised and unsupervised learning techniques (e.g., Random Forests, XGBoost, Logistic Regression, K-Means, Decision Trees).

- Data Visualisation: Experience building dashboards in Power BI, Tableau, or Looker.

- Tools & Environment: Familiarity with Git, Jupyter Notebooks, and cloud environments (AWS, GCP, or Azure).

Preferred Skills:

- Familiarity with LLMs, Generative AI APIs (e.g., OpenAI, Anthropic), or Retrieval-Augmented Generation (RAG) applications.

- Understanding of ML lifecycle frameworks (MLflow, Airflow) and MLOps fundamentals.

- Domain knowledge in industry verticals such as Retail/E-commerce, Healthcare, Financial Services, or Supply Chain.

Education:

- Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Applied Mathematics, Economics, Engineering, or a related quantitative field.

Pay: ₹500,000.00 - ₹1,000,000.00 per year

Benefits:

• Flexible schedule
• Provident Fund
• Work from home

Work Location: In person