As a Data Scientist, you will play a critical role in shaping our data strategy and solving complex business challenges through the innovative application of machine learning. You will move beyond simply executing on requirements; you will be a thought partner who seeks out opportunities, defines the right questions to ask, and drives projects from ambiguity to impactful business outcomes.

Senior Data Science

Your responsibilities

• End-to-End Model Ownership: Drive the entire machine learning lifecycle, from exploratory data analysis (EDA) and advanced feature engineering to model training, validation, deployment, and post-launch monitoring for performance and concept drift.
• Problem Formulation: Translate ambiguous business requirements and domain challenges into well-defined technical problems, testable hypotheses, and robust machine learning solutions.
• Rigorous Experimentation: Design, test, and validate multiple modeling approaches to find the optimal solution, establishing clear and relevant evaluation metrics that directly align with business goals.
• Technical Implementation & Deployment: Utilize our Triple AI SageMaker environment to efficiently train, deploy, and manage scalable models in a production setting.
• Data Storytelling & Visualization: Communicate complex model outputs and data-driven insights through compelling storytelling and clear visualizations, empowering business stakeholders to make informed, data-backed decisions.
• Product-Oriented Mindset: Develop a deep understanding of the business domain and product vision, ensuring that your work is not just technically sound but also delivers tangible and measurable value to the end-user.
• Collaborative Innovation: Actively collaborate with engineers, product managers, and business leaders, fostering a culture of shared knowledge, open feedback, and continuous improvement.
• Proactive & Agile Impact: Embody an entrepreneurial spirit and an agile mindset, proactively identifying opportunities for impact and focusing on delivering concrete business results and outcomes over exhaustive documentation.

Our requirements

• Master's degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
• 3+ years of hands-on professional experience in a data science role focused on building and deploying machine learning models.
• Strong proficiency in Python and its core data science libraries (e.g., pandas, NumPy, scikit-learn, Matplotlib/Seaborn).
• Solid proficiency in SQL for complex data querying, transformation, and analysis.
• Experience building models for business applications such as forecasting, classification, clustering, or regression.
• Familiarity with at least one major cloud platform (AWS, GCP, Azure).
• 5+ years of experience in a product-focused data science environment.
• Ideally, direct hands-on experience using Amazon SageMaker for model development, training, and deployment.
• Proven experience implementing and managing model monitoring systems to detect data and model drift in a production environment is highly desired.
• A forward-looking interest in the application of Generative AI, with an enthusiasm to learn how to combine LLMs and other generative techniques with traditional machine l- earning as part of your professional development in the role.
• Hands-on experience with MLOps principles and tools (e.g., MLflow, Kubeflow, feature stores).
• Demonstrated ability to thrive in a dynamic environment, taking complete ownership of ambiguous problems and driving them to resolution.
• Exceptional communication and data storytelling skills, with a proven ability to listen, understand business context, and influence both technical and non-technical audiences.
• A strong portfolio of completed data science projects that demonstrates a focus on delivering business impact.