(Sr.) Data Scientist-Modeling

About the Role

We are looking for a Data Scientist with strong modeling capabilities who can turn complex business problems into production-ready machine learning solutions. You will own the full lifecycle from problem scoping and model development to deployment and monitoring, with a strong focus on ensuring models deliver measurable business impact.

Key Responsibilities

• Design, build, and deploy predictive models across time-series forecasting, anomaly detection, causal inference, and Monte Carlo simulation.
• Translate complex model outputs into clear business insights and actionable recommendations for non-technical stakeholders.
• Productionize models end-to-end, including REST API development, Docker containerization, and deployment on AWS, GCP, or Azure.
• Build and maintain MLOps capabilities, including model registry, version management, A/B testing, automated retraining, and model performance monitoring.
• Develop reusable feature engineering pipelines and contribute to Feature Store architecture and governance.
• Process and analyze large-scale datasets using Spark or equivalent distributed computing frameworks.
• Monitor data drift, model degradation, and performance decay to ensure long-term model reliability.
• Build necessary tools and infrastructure independently when existing systems are insufficient.

What We’re Looking For

• Strong hands-on experience in machine learning and statistical modeling, particularly in time-series forecasting, anomaly detection, causal inference, or simulation.
• Proficiency in Python and common modeling frameworks, with practical experience using techniques such as LSTM, Transformers, XGBoost, Isolation Forest, Autoencoders, or Prophet.
• Solid engineering experience in model deployment, REST APIs, Docker, cloud platforms, and MLOps practices.
• Experience with large-scale data processing and distributed computing tools such as Spark.
• Strong business acumen and ownership mindset, with the ability to translate ambiguous business problems into practical data science solutions and adapt quickly to changing requirements.

Nice to Have: Experience in fintech, e-commerce, logistics, or risk-related domains; exposure to LLM applications for structured or tabular data; or experience with real-time inference and streaming technologies such as Kafka or Flink.

What We Offer

• A collaborative and inclusive work environment.
• Opportunities for professional growth and development.
• The chance to play a pivotal role in shaping the future of our organization.