Overview
This role operates in close collaboration with an ML Engineering team. The
candidate is expected to have clear judgment about their responsibilities within
that ecosystem and the discipline to work with software engineering standards,
not just analysis standards.
Your Role and Responsibilities
- Experimental design and hypothesis testing applied to business problems.
- Understanding of causality: not just correlation but the ability to distinguish and
apply appropriate techniques. - Robust model validation: beyond accuracy, business metrics, bias analysis, and
subgroup behavior. - Professional use of Git as part of the usual workflow, not as a formality at
delivery time. - Organized and modular Python code: the candidate must produce deliverable
code, not just exploration notebooks. - Familiarity with experiment tracking tools (MLflow or equivalent) for
experiment traceability. - Ability to document models in a structured way: what it solves, with what data,
with what limitations. - Experience working under team standards: secure credential handling, data
versioning, project structure. - Responsible use of generative AI tools as assistants: with the critical ability to
review and validate what they produce. - Judgment to evaluate when agent systems or LLMs are the right tool and when
they are not.
REQUIREMENTS
- More than 5 years in data science, statistical analysis, or applied research roles.
- Documentable end-to-end projects: from problem definition to delivery of a
validated model. - Experience working with engineering teams (ML Engineers, Data Engineers) in
agile environments. - Work history in real code repositories (a shareable portfolio will be valued).
- Advanced mastery of Python as the primary and sole development language.
- Solid knowledge of data science and ML libraries: Scikit-Learn , XGBoost ,
LightGBM , Pandas , Polars , Statsmodels , SciPy . - Experience with deep learning models ( TensorFlow or PyTorch ) when the
problem justifies it. - Ability to work with data at scale: advanced SQL, PySpark for exploration
and transformation. - Access to and handling of data in cloud environments (GCS, Azure Blob
Storage).
Working at Motivus
- Grow Your Career: At Motivus, you will have the opportunity to expand your skills and take on leadership roles in diverse projects.
- Work on Emerging Tech: Be at the forefront of innovation by working with cutting-edge technologies and methodologies.
- Collaborative Environment: Work with talented teams that value your ideas and contributions.
- Learning and Development: We offer a range of L&D programs to help you advance your career.
- Client-Centric Approach: Contribute to solutions that create lasting value for global Fortune 500 clients.
- Culture of Innovation: We pursue innovation relentlessly, encouraging our teams to explore new ideas and technologies.
- Responsibility: We give back to the communities that support us and our clients.
- Learning & Development: Our employees lead, learn, and grow, supporting themselves and our clients.
