The Senior Data Scientist will work on data-driven initiatives to solve complex business challenges, leveraging advanced analytics, machine learning, and statistical modeling. This role requires expertise in translating data insights into actionable strategies and collaborating with cross-functional teams. Ideal candidates will have a strong background in analytics or tech-driven industries.

Key Responsibilities :

- KPI Design & Stakeholder Strategy : Partner with cross-functional stakeholders (e.g., Marketing, Finance) to define and propose business KPIs that are logically sound, reasonably challenging, and easily communicable to non-technical teams.

- Data Engineering & EDA : Clean, preprocess, and validate large, complex datasets (structured/unstructured). Perform deep-dive Exploratory Data Analysis (EDA) to identify patterns, ensure data integrity, and set the initial strategic direction for analysis.

- Personalization & Recommendation : Design and implement recommendation engines to enhance user engagement and brand trust, leveraging both classical and deep learning-based approaches.

- Iterative Model Development : Develop and deploy predictive models including customer behavior prediction, customer lifetime value (CLV), media mix modeling, and time-series forecastingusing Python and PyTorch.

- Advanced Segmentation : Lead customer behavioral analysis projects using unsupervised clustering techniques to drive personalized brand empowerment strategies.

- Continuous Improvement Loops : Systematically identify bottlenecks in model accuracy through rigorous evaluation and error analysis; iterate on model architectures and data features to consistently hit and exceed target KPIs.

- Collaborative Engineering & Privacy : Maintain production-grade code repositories using GitHub, ensuring version control and documentation are integrated into the R&D process while adhering to data privacy and ethical AI practices.

- Cutting-Edge Research : Research and implement state-of-the-art techniques, including LLMs/Generative AI, NLP, and Deep Learning, to enhance business strategies and solve brand empowerment challenges.

Required Qualifications :

- Education : Masters/PhD in Statistics, Computer Science, Econometrics, or related quantitative fields.

- Experience : 5+ years in data science, with proven expertise in:

- End-to-End Ownership : Proven experience leading projects from initial data preparation and KPI definition through to delivery of final business impact.

- Programming : Expert-level proficiency in Python, SQL, and Spark, along with deep practical knowledge of libraries such as Pandas, Scikit-learn, PyTorch, and PySpark.

- Modeling & Mathematical Expertise : Extensive experience in the architecture and implementation of Decision Trees, Regression models, and Deep Learning (including LLMs/NLP).

- Recommendation Systems : Deep expertise in building recommendation models, including Collaborative Filtering, Content-Based Filtering, Matrix Factorization, and Neural Collaborative Filtering (NCF).

- Analytical Methods : Strong command of Unsupervised Clustering/Segmentation techniques and modern Time-Series Forecasting methodologies.

- Experimental Methodology : Rigorous skills in offline evaluation, cross-validation techniques, and iterative hypothesis testing to improve model performance.

- Cloud Platforms : Hands-on experience architecting and deploying solutions in Azure, Databricks, Snowflake, or AWS.

- Development Environment & Version Control : Advanced proficiency with Linux/Unix environments and VS Code, alongside GitHub/Git for collaborative development, branching strategies, and CI/CD workflows.

- Soft Skills : Exceptional stakeholder management (translating complex data into simple stories), professional time management, and a relentless problem-solving mindset.