Overview
We are seeking a highly motivated Data Scientist I with a strong foundation in machine learning, statistics, and probability. The ideal candidate will be capable of developing and productionizing machine learning models to solve practical business problems.
Key Responsibilities
Apply fundamental concepts of machine learning, statistics, and probability to design and develop predictive models.
Collaborate with cross-functional teams (engineering, product, business) to transform business requirements into data-driven solutions.
Productionize ML models, ensuring smooth deployment, scalability, and monitoring in real-world settings.
Analyze large datasets using statistical and computational methods to extract actionable insights.
Evaluate, tune, and interpret models for performance and business relevance.
Document methodologies, workflows, and experiments for reproducibility and cross-team knowledge sharing.
Required Qualifications
Bachelor’s or master’s degree in a quantitative field (Computer Science, Statistics, Mathematics, or similar).
Strong understanding of ML/statistics/probability fundamentals (regression, classification, hypothesis testing, probability distributions, etc.).
Hands-on experience in one or more programming languages (Python, R, etc.) and ML libraries (scikit-learn, TensorFlow, PyTorch).
Knowledge of model deployment tools and frameworks (Docker, Flask/FastAPI, MLflow, etc.).
Ability to implement and deploy models in a production environment.
Familiarity with recent advancements in AI/ML (transformers, large language models, generative AI, etc.).
Experience with cloud platforms (AWS, GCP, Azure) is a plus.
Strong problem-solving, communication, and teamwork skills.
Preferred Skills
Experience with deep learning frameworks.
Exposure to MLOps practices (CI/CD for ML, monitoring and retraining pipelines).
Publications or side projects demonstrating innovative use of contemporary AI methods.
What You Get to Work On
Model user behavior patterns to solve high-impact, business-specific use cases.
Work on complex savings account transactions data, mining actionable and meaningful insights.
Extend and apply advanced deep learning, ML, and LLM-based models to production-scale problems.
Build predictive and prescriptive models that directly influence product strategy and customer experience.
Collaborate with product and engineering teams to integrate ML-driven intelligence into customer-facing features.
Experiment with cutting-edge AI techniques (generative AI, transformers, reinforcement learning) for real-world financial applications.