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:
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Lead the design and deployment of enterprise-scale forecasting systems with a focus on time-series modelling, performance monitoring, and long-running production systems.
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Develop robust temporal data quality frameworks to handle missing data, irregular timing, and outliers directly within production pipelines.
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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.
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Design interpretable ensemble approaches, combining multiple regression models with trend and seasonal decomposition to isolate key demand drivers.
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Engineer reusable model training and deployment pipelines to improve consistency and reduce setup time across data science teams.
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Implement rigorous performance monitoring to detect forecast issues, analyze drift, identify unusual shifts in data patterns, and prevent downstream model degradation.
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Spearhead the integration and monitoring of LLM-based systems, including stability analysis and cost-forecasting modules within Azure AI and AWS Bedrock workflows.
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Drive AI governance by establishing model monitoring, safety frameworks, and performance controls for secure enterprise adoption.
Required Skills:
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Experience: 6-8+ years of proven expertise in building and deploying scalable machine learning models in enterprise environments.
- Programming & Big Data: Advanced proficiency in Python, PySpark, SQL. Strong hands-on experience with Databricks is mandatory.
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Machine Learning Core: Random Forest, Scikit-learn, K-Means/KNN, Linear Regression, and Naive Bayes.
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Time Series & NLP: Strong background in temporal data exploration, pattern recognition, anomaly detection, and NLP tools (NLTK, Spacy).
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MLOps & Deployment: Expertise in MLOps, LLM Ops, DevOps, Docker, Git/GitHub, and cloud deployment pipelines (AWS, Azure).
Optional/Nice-to-have Skills:
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MLOps: Model tracking, monitoring, CI/CD with MLflow, Kubeflow, etc.
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Big Data Tools: Spark, Databricks, or Hadoop ecosystem familiarity
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Experiment Tracking: Tools like Weights & Biases, MLflow
Certifications (Preferred but not Mandatory):
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Google Cloud or Azure AI Engineer / Data Scientist Associate
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Databricks Certified Machine Learning Professional
