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Data Scientist
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- Posted 11 days ago
- Be among the first 20 applicants
Job Description
- Experience: 4–6+ years delivering end-to-end data science projects.
- Education: Master's or PhD in Data Science, Machine Learning, Statistics, Computer Science, Applied Mathematics, or related quantitative field (required from this level onward).
- Core stack: Python, Spark, Git; ML frameworks; Databricks/MLflow (or equivalent); cloud basics.
- Translate business needs into ML/AI problem statements and measurable success metrics.
- Develop ML models and, where relevant, GenAI components (e.g., retrieval-augmented generation, prompt pipelines) with clear evaluation criteria.
- Run evaluation: offline metrics, error analysis, bias checks, and monitoring baselines; document decisions and assumptions.
- Communicate results and limitations clearly to technical and non-technical stakeholders; support adoption in workflows.
- Python (pandas, numpy) + Git for reproducible development
- Databricks (Notebooks, Workflows) for development and orchestration
- ML flow (experiments, tracking, model registry) for lifecycle management
- Azure (cloud services; where relevant Azure OpenAI and Azure AI Foundry for GenAI build/evaluation)
- Databricks Mosaic AI (including Mosaic AI Model Serving) for GenAI delivery in the lakehouse
- Databricks Vector Search for RAG retrieval patterns
- Unity Catalog for governed data and model access (where applicable)
- Lakehouse Monitoring / model monitoring for quality and drift (where applicable)
More Info
Key Skills
Lakehouse Monitoring
Databricks Mosaic AI
Databricks Vector Search
MLflow
Azure OpenAI
Azure AI Foundry
Unity Catalog
ML frameworks
About Company
Skills:
statistical data analysis , snowflake , Hypothesis Testing, Tableau, Multivariate Analysis, D3, Docker, Python, BigQuery, Sql, Git, Predictive Modeling, Spark, FastAPI, Kubernetes, drift analysis, data visualization tools, ML data science libraries, RAG architectures, sampling methods, LLMs, Time Series techniques, plotly, Classification, prompt engineering, cloud data platforms
Skills:
.NET, AI ML, Python, Agent governance frameworks, Agentic AI workflows, Agentic Document Extraction, MCP-based architectures, AI infrastructure cost planning, Document AI pipelines, Agentic RAG, Document understanding
Skills:
Spark SQL, CSS, PostgreSQL, Pyspark, HTML, Sql, Django, React, Git, Typescript, Flask, Databricks, FastAPI, Rest Apis, Python, AWS, CI CD, Delta Lake
Skills:
Machine Learning, Pyspark, Big Data, Sql, Deep Learning, Tensorflow, DataBricks, Jenkins, Git, Azure Data Factory, Keras, Python, Spark MLLib
Skills:
snowflake , Nosql, Docker, Python, Sql, AWS, LangChain, LangGraph, Data Lakes, CI/CD