Job description

We are seeking a high-impact Senior Applied Data Scientist to join our team, where you will bridge the gap between advanced mathematical modeling and tangible business value. You will lead the design and implementation of sophisticated analytical solutions that solve complex problems for our global clients.

In this role, you will act as a strategic partner, working alongside consultants and engineers to translate business requirements into scalable data products. Unlike theoretical research, your focus will be applied—meaning you will be responsible for the end-to-end lifecycle of a model, from exploratory data analysis and feature engineering to production-grade deployment and monitoring.

You are not just a model builder; you are a problem solver who understands the "why" behind the data. You will navigate the nuances of predictive analytics, causal inference, and optimization to ensure our clients reach their transformation goals with precision and efficiency. We invest in your growth by providing access to cutting-edge tools, global centers of excellence, and a collaborative environment where technical rigor meets business strategy.

Key Responsibilities

• End-to-End ML Development: Lead the discovery, development, and deployment of machine learning models (Regression, Classification, Clustering, Time-Series Forecasting) to solve industry-specific challenges.

• Business Translation: Work closely with stakeholders to identify high-value use cases and translate vague business problems into concrete technical roadmaps.

• Data Engineering & Wrangling: Design and optimize data pipelines and feature stores, ensuring data quality and integrity across diverse environments (SQL, NoSQL, Data Lakes).

• Advanced Analytics: Apply statistical rigor to experimental design, including A/B testing and causal inference, to validate the impact of business interventions.

• Model Productization: Partner with ML Engineers to implement MLOps best practices, including versioning, automated testing, and CI/CD for model deployment.

• Scalable Architecture: Design robust, reusable analytical frameworks that can be scaled across different client engagements or internal Sia products.

• Stakeholder Management: Communicate complex technical findings to non-technical executive audiences through compelling storytelling and data visualization.

• Mentorship: Act as a technical lead, providing code reviews, architectural guidance, and mentorship to junior data scientists within the team.

• Innovation: Stay at the forefront of the field, evaluating and integrating emerging techniques in Deep Learning, Reinforcement Learning, or Optimization into the Sia toolkits.