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Mid-level ML role, metro location, broad skillset and generalist Data Scientist title drive high applicant competition.
Specialised time-series and Databricks requirements make cross-industry transfers moderately sensitive.
Explicit 6+ years requirement plus mandatory Databricks, MLOps, and cloud skills increase shortlisting strictness.
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Lead design and deployment of enterprise-scale forecasting systems focusing on time-series modelling and long-term demand prediction.
Develop and maintain robust temporal data quality frameworks and model monitoring systems to ensure production pipeline reliability.
Spearhead integration and monitoring of LLM-based AI systems and drive AI governance with safety and performance controls for enterprise adoption.
6-8+ years of experience in building and deploying scalable machine learning models in enterprise environments.
Advanced proficiency in Python, PySpark, SQL; mandatory hands-on experience with Databricks.
Strong expertise in time series modelling, machine learning algorithms (Random Forest, Scikit-learn, K-Means/KNN, Linear Regression, Naive Bayes), and NLP tools (NLTK, Spacy).
Experience with MLOps, DevOps, containerization (Docker), version control (Git/GitHub), and cloud deployment (AWS, Azure).
Experienced in leading end-to-end forecasting system projects with emphasis on temporal data and long-running production environments.
Proficient in building reusable model pipelines and driving AI governance frameworks for secure enterprise AI adoption.
Skilled in integrating modern AI/ML technologies including large language models within cloud platforms (Azure AI, AWS Bedrock).