About The Role

We are seeking an experienced and impact-driven Data Scientist to join our team. In this role, you will be responsible for delivering end-to-end data science solutions, from problem definition and data exploration to model deployment and evaluation. You will act as a technical bridge between complex data environments and business stakeholders, influencing the technical direction of projects and ensuring that our models provide quantifiable value and drive key business objectives.

End-to-End Model Development: Design, execute, and deploy impactful DS projects. This includes performing Exploratory Data Analysis (EDA), feature engineering, and implementing advanced modeling techniques tailored to unique data characteristics (e.g., XGBoost, TensorFlow, or PyTorch).
Engineering & Reproducibility: Consistently deliver high-quality, reproducible code. You will implement robust error handling, conduct comprehensive model validation, and establish patterns/standards for code clarity and maintainability within a Git-based workflow.
MLOps & Deployment: Deploy models using established CI/CD pipelines and tools (e.g., MLflow, Kubeflow). You will be responsible for designing scalable deployment strategies, configuring model monitoring, alerting, and troubleshooting prediction service issues.
Cloud Operations : Implement data processing pipelines in the cloud (e.g., Spark, Dataflow) and manage ML services effectively. You will optimize cloud resources for cost-efficiency, security, and reliability.
Experimentation & Observability: Design and execute experiments (A/B tests) and create dashboards to monitor model health, feature drift, prediction distributions, and performance SLOs (Service Level Objectives).
Collaboration & Influence: Translate business needs into technical tasks and build consensus on analytical approaches. You will mentor junior team members through constructive code/methodology reviews and communicate findings effectively to varied audiences.
Documentation: Produce clear technical design documents and architectural roadmaps for data sources, transformations, assumptions, and experiments to ensure long-term scalability and maintainability.
Education: Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, or a related quantitative field.
Technical Mastery: Proficiency in Python/R and SQL. Advanced experience with DS libraries such as Scikit-learn, and exposure to deep learning frameworks (TensorFlow/PyTorch).
Cloud Proficiency: Hands-on experience with cloud data storage and compute (e.g., GCS, Vertex AI), build pipelines, and partnering with engineering to operationalize production-ready models.
Software Engineering: Strong understanding of Git workflows, unit testing, and advocating for quality tools/practices in Data Science workflows.
Business Acumen: Proven ability to partner with stakeholders to define data-driven strategies and quantifiably contribute to business goals via models and insights.
Communication: Demonstrating strong visualization skills and the ability to socialize model results into strategic implications for leadership, covering both technical and non-technical audiences.