Job Description

• Design, prototype, and implement ML models for financial applications, including predictive analytics, risk modeling, and trading algorithms.
• Identify, collect, clean, and preprocess structured and unstructured financial data from multiple sources, including web data extraction.
• Build and optimize training pipelines for experimentation and production deployment. Work with engineers to deploy and scale ML models in real-world environments.
• Fine-tune and optimize foundational AI models, including LLMs, RAG, and agent-based applications to enhance decision-making and automation.
• Work closely with cross-functional teams, including software engineers, data scientists, and business stakeholders, to translate business needs into effective ML solutions.
• Define the data science roadmap and establish best practices for model development, versioning, and deployment. Stay updated on industry trends and emerging technologies.

Job Requirements

• Ability to translate complex financial and business challenges into ML-driven solutions and communicate findings effectively.
• Solid understanding of financial markets, algorithmic trading, and risk management strategies.
• Experience in classical ML models, inference systems, and fine-tuning generative AI models.
• Strong skills in data scraping, extraction, transformation, and pipeline development for large-scale datasets.
• Proficiency in Python and ML frameworks like PyTorch, TensorFlow, Keras, Scikit-learn, NumPy, and Pandas.
• Strong coding ability with experience in Git, CI/CD, Docker, and Kubernetes.
• Familiarity with MLflow, Kubeflow, Airflow, and cloud-based ML services for model management and deployment.
• Shown a track record of being able to execute with minimal high-level guidance from C-levels.

Nice to have:

• Experience with Spark, Dask, Snowflake, and BigQuery for large-scale data handling.
• Knowledge of Graph Neural Networks (GNNs) and time-series forecasting for financial applications.
• Experience with LLMs, RAG, LangChain, LlamaIndex, and AI-driven automation.
• Familiarity with AWS, GCP, or Azure for scalable ML model deployment.

Skills