Data Scientist – Machine Learning

As a Data Scientist you will take full ownership of end-to-end model development cycles—from ideation and exploratory data analysis through deployment and performance monitoring. You will collaborate closely with engineering, product, and domain-expert teams to discover new signal sources, architect robust feature pipelines, and deliver models that materially improve portfolio performance, user engagement, and internal decision-making.

What You’ll Do

Model Development & Research

• Design, prototype, and validate supervised and unsupervised ML models (e.g., gradient boosting, Transformers, temporal graph networks) for prediction, classification, clustering, and anomaly detection.
• Own the complete ML lifecycle: data gathering, feature engineering, algorithm selection, hyperparameter tuning, model evaluation/interpretation, and production deployment.

Data Engineering & Pipeline Automation

• Build scalable ETL workflows that transform terabytes of structured and unstructured data—time-series, textual news, alternative data, clickstreams—into model-ready feature stores.
• Collaborate with data engineering to optimize data schemas, indexing strategies, and distributed processing on Spark, Snowflake, or Airflow-based DAGs.

Experimentation & Performance Tracking

• Establish rigorous offline and live A/B testing frameworks to quantify ROI, mitigate information leakage, and ensure statistical validity across multiple market regimes.
• Implement monitoring dashboards for drift detection, re-training triggers, and post-deployment model governance.

Cross-functional Partnership

• Translate complex model outputs into intuitive visualizations and insights for product managers, executives, and non-technical stakeholders.
• Mentor junior scientists and review code/experiments to uphold best practices in reproducibility and documentation.

What We’re Looking For

• Technical Skills
• 3 + years building and deploying ML models in production (Python preferred; familiarity with Scala/Java a plus).
• Deep hands-on experience with at least two of: PyTorch, TensorFlow/Keras, XGBoost/LightGBM, Hugging Face Transformers, HDBSCAN/graph analytics libraries.
• Proficiency in SQL and one or more big-data ecosystems (Spark, Dask, Google BigQuery, or similar).
• Comfort with MLOps tooling (MLflow, Kubeflow, SageMaker, Vertex AI) and containerization (Docker/K8s).
• Solid understanding of statistical inference, time-series analysis, and model explainability techniques (SHAP, counterfactuals, LIME).

Domain & Soft Skills

• Strong problem-solving mindset, self-directed research skills, and the ability to simplify complex ideas for diverse audiences.
• Track record of shipping impactful features in a rapid, iterative, and collaborative environment.
• Excellent written and verbal communication in English.

Nice-to-Haves

• Experience with reinforcement learning and recommendation engines.
• Knowledge of retrieval-augmented generation (RAG) pipelines and large-language-model fine-tuning.
• Familiarity with cloud infrastructure costs and optimization strategies (AWS/GCP/Azure).

Job Type: Full-time