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 Workplace 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), qualified time management, and a relentless problem-solving mindset. This job was posted by Anand Prabha from Mico Inc.

📌 Data Scientist (Bengaluru)
🏢 Mico
📍 Bengaluru