3 days ago
Hyderābād, IndiaMid Level / Senior
Responsibilities
- Design, develop, deploy, optimize, and scale machine learning and GenAI models for complex financial data problems.
- Develop and fine-tune LLMs and generative AI applications for structured and unstructured data using prompt engineering and retrieval-augmented generation.
- Build Python-based analytics pipelines and data-processing workflows with Databricks, Spark, Delta Lake, Feature Stores, and MLOps practices.
- Apply machine learning and data engineering techniques to entity disambiguation, real-time risk analytics, NLP, graph data modeling, and anomaly detection.
- Use cloud data ecosystems, ETL/ELT, data lakes, and real-time streaming to deliver production solutions at scale.
- Translate research papers and new ML techniques into production systems.
- Collaborate with engineers, product managers, and UX teams, and mentor junior data scientists and engineers.
Requirements
- Bachelor’s degree in Engineering, Computer Science, or equivalent.
- 3–5 years of professional experience in machine learning, Python programming, and data engineering.
- Deep expertise in Python, NumPy, Pandas, PySpark, FastAPI, and related tools, plus experience with TensorFlow, PyTorch, and Transformers.
- Practical GenAI experience including LLM training or fine-tuning, OpenAI, HuggingFace, Google Gemini, prompt engineering, and RAG.
- Hands-on Databricks experience, including Workspace, MLflow, Delta Lake, and Notebooks.
- Strong knowledge of AWS, Azure, GCP, Docker, Kubernetes, ETL/ELT, data lakes, Kafka, and Spark Streaming.
- Familiarity with MLOps toolchains including MLflow, Airflow, Feature Store, and CI/CD.
- Published research or open-source contributions are a plus, along with effective communication and collaboration skills.
Benefits
- Health, life, and disability insurance; retirement savings plans; a discounted employee stock purchase program; paid holidays, family leave, and company-wide wellness days.
- Flexible work accommodations supporting work/life harmony.
- Career progression planning with dedicated monthly learning and development time.
- Business Resource Groups, volunteerism and sustainability opportunities, and a collaborative global community.
Tech Stack
Apache AirflowApache KafkaApache SparkAWSAzureDatabricksDockerFastAPIFlaskGoogle Cloud PlatformHugging Face TransformersKubernetesMLflowNumPyPandasPythonPyTorchSQLTensorFlow
Categories
About FactSet
FactSet builds a financial data and analytics platform used by asset managers, investment banks, hedge funds, and wealth managers for research, portfolio management, trading, and risk. Its products include the FactSet workstation, data feeds and APIs, and workflow tools like OMS/EMS, sold primarily via subscription licensing. Founded in 1978 and headquartered in Norwalk, Connecticut, FactSet is a public company listed on the NYSE (ticker: FDS).