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.
Responsibilities
- KPI Design and 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 and 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 and 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 forecasting using 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 and 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.
Requirements
- Education: Master's/PhD in Statistics, Computer Science, Econometrics, or related quantitative fields.
- Experience: 5+ years in data science, with proven expertise.
- 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 and 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 and 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.
This job was posted by Anand Prabha from Mico Inc.
