Sr. Data Scientist II

Job Description: Senior Data Scientist (6-8+ Years Experience)
**Job Summary:**We are seeking a highly experienced and innovative Senior Data Scientist with over 6+ years of expertise in core data science concepts and around 2 years of focused, hands-on experience in Machine Learning model development. You will lead strategic AI/ML initiatives, mentor junior data scientists, and deliver intelligent solutions that drive business value using both classical and modern machine learning techniques.
Key Responsibilities:

1. Lead the design and deployment of enterprise-scale forecasting systems with a focus on time-series modelling, performance monitoring, and long-running production systems.
2. Develop robust temporal data quality frameworks to handle missing data, irregular timing, and outliers directly within production pipelines.
3. Create and maintain forecasting systems for demand prediction, generating long-term (e.g., 24-month) forecasts using historical patterns, external factors, and advanced feature engineering.
4. Design interpretable ensemble approaches, combining multiple regression models with trend and seasonal decomposition to isolate key demand drivers.
5. Engineer reusable model training and deployment pipelines to improve consistency and reduce setup time across data science teams.
6. Implement rigorous performance monitoring to detect forecast issues, analyze drift, identify unusual shifts in data patterns, and prevent downstream model degradation.
7. Spearhead the integration and monitoring of LLM-based systems, including stability analysis and cost-forecasting modules within Azure AI and AWS Bedrock workflows.
8. Drive AI governance by establishing model monitoring, safety frameworks, and performance controls for secure enterprise adoption.

Required Skills:

1. Experience: 6-8+ years of proven expertise in building and deploying scalable machine learning models in enterprise environments.
2. Programming & Big Data: Advanced proficiency in Python, PySpark, SQL. Strong hands-on experience with Databricks is mandatory.
3. Machine Learning Core: Random Forest, Scikit-learn, K-Means/KNN, Linear Regression, and Naive Bayes.
4. Time Series & NLP: Strong background in temporal data exploration, pattern recognition, anomaly detection, and NLP tools (NLTK, Spacy).
5. MLOps & Deployment: Expertise in MLOps, LLM Ops, DevOps, Docker, Git/GitHub, and cloud deployment pipelines (AWS, Azure).

Optional/Nice-to-have Skills:

1. MLOps: Model tracking, monitoring, CI/CD with MLflow, Kubeflow, etc.
2. Big Data Tools: Spark, Databricks, or Hadoop ecosystem familiarity
3. Experiment Tracking: Tools like Weights & Biases, MLflow

Certifications (Preferred but not Mandatory):

1. Google Cloud or Azure AI Engineer / Data Scientist Associate
2. Databricks Certified Machine Learning Professional