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.