Senior Data Scientist - AI/ML

Location: Bangalore

Work Mode: Hybrid

Experience: 5+ Years

We are looking for a Senior Data Scientist with a strong foundation in Machine Learning, Algorithms, Statistical Modeling, and AI-native development to contribute to advanced AI initiatives and real-time analytics platforms.

This is a hands-on, technically deep role for professionals passionate about building scalable and intelligent ML solutions.

Key Responsibilities

- Design, build, and optimize ML models for real-world, large-scale datasets.

- Develop solutions for predictive modeling, anomaly detection, forecasting, and automated alerting.

- Apply classical ML techniques including regression, classification, clustering, and ensemble methods.

- Work with deep learning architectures such as CNNs, RNNs/LSTMs, Transformers, and attention mechanisms.

- Analyze time-series and complex real-time data.

- Develop POCs and take ML solutions from experimentation to production.

- Process and analyze large-scale datasets using Ray and Spark.

- Collaborate with engineering and product teams to integrate ML models into production systems.

- Contribute to AI roadmaps and AI-driven product initiatives.

- Leverage AI coding tools such as Claude, Cursor, or GitHub Copilot for development, debugging, prototyping, and solution design.

Required Skills

- 5+ years of experience in Data Science / Applied Machine Learning.

- Advanced proficiency in Python; Scala or similar languages is a plus.

- Strong knowledge of:

- Machine Learning & Statistical Modeling

- Regression, Classification & Ensemble Methods

- Clustering & Dimensionality Reduction

- Neural Networks & Deep Learning

- CNNs, RNNs/LSTMs, Transformers & Attention

- Optimization, Bayesian & Probabilistic Modeling

- Strong ability to take models from POC to scalable production solutions.

- Hands-on experience with AI-assisted development tools.

- Strong analytical, problem-solving, and communication skills.

Good to Have

- Experience with real-time data processing and streaming analytics.

- Strong knowledge of time-series analysis, forecasting, seasonality, and change-point detection.

- Experience building AI-powered alerting or anomaly detection systems.

- Knowledge of MLOps and cloud-based ML deployment.

- Experience with model monitoring, versioning, retraining pipelines, or production ML systems.

- Experience integrating or fine-tuning LLMs/foundation models.