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.
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.
requirements-expected :
Masters 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.
