Whitetable
Machine Learning & Data Science Engineer
- Compensation
- Not disclosed
- Location
- Bangalore; Gurgaon/Gurugram
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Data science
- Posted
Skills
Must have
- - 5+ years of hands-on experience in Machine Learning, Time Series Analysis, and Causal Analytics.
- - Bachelors degree in Computer Science, Mathematics or Statistics. Masters or PhD is a plus
- - Strong proficiency in Python and libraries like Scikit-Learn, TensorFlow, PyTorch, Statsmodels, Prophet, or similar.
- - Deep understanding of time-series modeling, forecasting, and anomaly detection techniques.
- - Expertise in causal inference, Bayesian statistics, and causal graph modeling.
- - Experience in ML Ops, model evaluation, and deployment in production environments.
- - Working knowledge of databases and data processing frameworks (SQL, Spark, Dask, etc.).
- - Experience in observability, monitoring, or AI-driven system diagnostics is a big plus.
- - Background in AI Agent evaluation and optimization is a plus.
- - Working with LLMs, fine-tuning LLMs, LLM Ops, LLM Agents is a big plus
- Our Values :
- - Loyalty & Long-term Commitment - We invest in people who invest in us.
- - Opinionated yet Open-Minded - We value strong perspectives but encourage constructive discussions.
- - Passion - We seek individuals who are passionate about their craft.
- - Humility & Integrity - Honest, transparent, and accountable team members are key.
- - Adaptability & Self-Sufficiency - Ability to thrive in a fast-paced and evolving environment.
- - Build Fast and Break Fast - We believe in rapid iteration and learning from failures.
- What You'll Work On:
- Comp
Full description
What You'll Work On :
- Develop state-of-the-art time series models for anomaly detection and forecasting in observability data.
- Design a root cause analysis system using LLMs, causal analysis, machine learning and anomaly detection algorithms.
- Develop Large Language Models for time series analysis
- Create highly scalable ML pipelines for real-time monitoring and alerting.
- Build and maintain ML Ops workflows for model deployment, evaluation, monitoring, and updates.
- Build frameworks to evaluate AI agents.
- Handle large datasets using Python and its ML ecosystem (e.g., NumPy, Pandas, Scikit-Learn, TensorFlow, PyTorch, Statsmodels).
- Use Bayesian methods, Granger causality, counterfactual analysis, and other techniques to derive meaningful system insights.
- Collaborate with other teams to deploy ML-driven observability solutions in production.
**What We're Looking For :
- 5+ years of hands-on experience in Machine Learning, Time Series Analysis, and Causal Analytics.
- Bachelors degree in Computer Science, Mathematics or Statistics. Masters or PhD is a plus
- Strong proficiency in Python and libraries like Scikit-Learn, TensorFlow, PyTorch, Statsmodels, Prophet, or similar.
- Deep understanding of time-series modeling, forecasting, and anomaly detection techniques.
- Expertise in causal inference, Bayesian statistics, and causal graph modeling.
- Experience in ML Ops, model evaluation, and deployment in production environments.
- Working knowledge of databases and data processing frameworks (SQL, Spark, Dask, etc.).
- Experience in observability, monitoring, or AI-driven system diagnostics is a big plus.
- Background in AI Agent evaluation and optimization is a plus.
- Working with LLMs, fine-tuning LLMs, LLM Ops, LLM Agents is a big plus
Our Values :
- Loyalty & Long-term Commitment - We invest in people who invest in us.
- Opinionated yet Open-Minded - We value strong perspectives but encourage constructive discussions.
- Passion - We seek individuals who are passionate about their craft.
- Humility & Integrity - Honest, transparent, and accountable team members are key.
- Adaptability & Self-Sufficiency - Ability to thrive in a fast-paced and evolving environment.