Machine Learning Scientist II

Machine Learning Scientist (MLS-2) – New Initiatives

About Nykaa

Nykaa is India’s leading beauty and lifestyle destination. We are a consumer-tech company offering a wide portfolio of beauty, personal care, and fashion products across online platforms and retail stores.

We are powered by technology and data, and our Data Science team plays a critical role in building intelligent, personalized, and seamless experiences for millions of customers. As we scale, we are investing heavily in foundational ML systems that enable real-time decisioning, automation, and GenAI-powered customer experiences.

Role Overview

As an MLS-2 (Machine Learning Scientist), you will work on high-impact ML projects across demand forecasting, supply chain optimization, marketing personalization, and fraud risk detection. You will contribute to model development, experimentation, and deployment of scalable ML solutions that directly influence inventory efficiency, marketing ROI, delivery predictability, and platform integrity. This is a hands-on role where you will work with senior scientists and engineers, translate business requirements into well-defined modeling tasks, and ensure high-quality model delivery.

Key Responsibilities

1. Machine Learning & Modeling

• Build and improve forecasting models using statistical and ML techniques such as ARIMA, Prophet, XGBoost, LSTMs, and transformer-based architectures under guidance from senior scientists.
• Develop models for coupon optimization, price elasticity, uplift modeling, and marketing attribution to improve personalization and campaign efficiency.
• Contribute to fraud detection models using anomaly detection, graph-based techniques, and unsupervised learning.

2. Supply Chain & Operational Modeling

• Support the development of algorithms for inventory planning, SKU allocation, and warehouse optimization using linear/integer programming and heuristic methods.
• Work on improving Estimated Delivery Date (EDD) accuracy using spatiotemporal, carrier, and operational signals.

3. Data Science & Analytics

• Translate loosely defined business problems into structured ML tasks.
• Conduct exploratory data analysis, feature engineering, model training, evaluation, and documentation.
• Build and maintain data pipelines in collaboration with Data Engineering teams.

4. Deployment & Productionization

• Work with engineering teams to productionize ML models using frameworks like MLflow, SageMaker, Databricks, and Spark.
• Monitor model performance, detect drifts, and contribute to continuous model improvements.

5. Collaboration & Communication

• Collaborate with Product, Engineering, Operations, and Marketing teams to deliver data-driven insights and solutions.
• Present analysis and model outcomes to cross-functional partners in a clear and structured manner.

Qualifications & Skills

Experience

• 2–5 years of hands-on experience in Machine Learning, Applied Statistics, or related fields.

Education

• Bachelor’s or Master’s degree in Computer Science, Statistics, Applied Mathematics, Data Science, or a related quantitative discipline.

Technical Skills

• Strong foundation in ML concepts including regression, tree-based methods, regularization, bias–variance tradeoffs, and probabilistic modeling.
• Experience with time-series modeling (ARIMA, ETS, Prophet); exposure to deep-learning-based forecasters (LSTM, TFT, DeepAR) is a plus.
• Working knowledge of optimization techniques (linear programming, heuristics, or RL concepts) is desirable.
• Good coding skills in Python and experience with ML libraries such as Scikit-learn, XGBoost, PyTorch/TensorFlow, Prophet, and PyMC/Stan (nice-to-have).
• Experience working with distributed data environments like Spark, Databricks, or AWS is a plus.

Business Skills

• Ability to break down business problems into analytical tasks.
• Strong analytical reasoning and problem-solving skills.
• Clear written and verbal communication skills.

What You’ll Learn & Grow Into

• Building production-grade ML systems at scale.
• Designing optimization solutions for supply chain and logistics.
• Deep familiarity with causal inference and marketing science.
• Working with large-scale distributed data systems.
• Contributing to real-time decisioning systems and GenAI-powered experiences.

Qs to expect on:

• ML Foundational concepts like Bias-Variance Trade-Offs, Linear/Logistic regression, Tree Methods, Maximum likelihood Estimation, etc…
• Statistical foundational concepts like Central Limit Theorem, A/B testing, statistical distributions, etc.
• The Error/Cost functions behind the ML methods that you recently worked on, and intuitions behind them.
• Time-Series Forecasting methods, including classical (ARIMA, ETS) and modern DL based methods(DeepAR, etc.)
• Formulate Business problems as optimization problems with techniques from Linear/Integer Programming, Genetic Algorithms, Reinforcement Learning, and heuristic optimization.
• Observational Causal Inference Methods like Inverse Propensity Weighting, Doubly Robust Methods. Foundational concepts like Causal Assumptions, Potential Outcome Framework, etc.