Job Specifications
Machine Learning Engineer
ABOUT ODAIA
ODAIA noun
o·da·ia | 'oh-day-yeah
An Ancient Greek word referring to “tools of the trade.”
ODAIA is a remote first organization, all our positions are WFH with frequent company and team based socials, events and meetings in-person and virtually.
ODAIA works with pharma commercial teams to grow their brands and engage with the right healthcare professionals (HCPs) at the right time. ODAIA's SaaS AI platform turns complex data into personalized insights, enabling commercial teams to understand their customers - (HCPs), predict prescribing behaviors and make informed decisions that bring therapies to patients faster.
ODAIAns (what we call ourselves) are inspired to reinvent the future of how non-technical people leverage data in their day-to-day lives. We are passionate about solving complex problems in data, AI, engineering, design, and product, so our customers don’t have to. We live by the notion that “simplicity is the ultimate sophistication;” and making simplicity scalable is an even bigger challenge. That’s why we have a crazy talented team led by serial entrepreneurs, tech veterans, and life sciences experts.
To learn more visit odaia.ai.
OUR MISSION
Reducing patients’ time to therapy by facilitating meaningful interactions with healthcare providers, through human-centric software powered by AI.
We’re also on a mission to build an innovative, diverse, and ego-free business, where trust, innovation and ownership are valued. You’re on a mission too? We’re here for it. We put an emphasis on career development for our employees, and the opportunities to grow are extensive.
WHAT’S ON OFFER
This is an excellent opportunity to join a rapidly growing innovator in a technically challenging and rewarding role. Reporting to the Senior Engineering Development Manager, the Machine Learning Engineer will lead the development and enhancement of cutting-edge machine learning models and advanced analytics that directly drive customer value. Leveraging deep expertise in ML algorithms and large-scale data modeling, you will conduct research, experiment with novel techniques, and deploy innovative solutions that shape our predictive insights. You'll collaborate closely with data engineers and product teams across the organization to ensure our products leverage best-in-class AI capabilities, delivering robust, scalable, and impactful solutions.
WHAT YOU WILL DO
Develop, implement, and optimize advanced machine learning models, focusing on predictive modeling, time-series analysis, causal inference, and hierarchical modeling
Design and build scalable ML pipelines using Python, SQL, Dagster, and Kubernetes to ensure efficient data workflows and robust deployment
Rigorously evaluate and iteratively enhance machine learning model performance using appropriate statistical methods and validation frameworks
Own end-to-end responsibility for the ML system lifecycle, from problem definition and exploratory analysis to deployment, monitoring, and ongoing iteration
Clearly articulate complex modeling concepts and analytical outcomes to both technical colleagues and non-technical business stakeholders.
Ensure solutions delivered are scalable, high-quality, and promptly address evolving customer needs and business objectives
Continuously explore and integrate advances in ML research and statistical methodologies into ODAIA’s frameworks
Foster collaboration and knowledge sharing across teams, contributing to a culture of innovation and technical excellence
Please include a cover letter with your application outlining your relevant experience and interest in this role, you can describe a project you’ve worked on that relates to one of the “What you bring” or “What you will do” points, and share how you’d expand on it here at ODAIA
WHAT YOU BRING
Professional experience in developing, deploying and monitoring advanced machine learning models, particularly in analytics-intensive or production environments
Strong foundation in software engineering best practices, including version control, clean and maintainable code, documentation, reproducible research, and collaborative development workflows
Practical experience with cloud-based ML environments (AWS, Azure, or GCP) and modern data science toolchains, including containerized and distributed compute
Deep proficiency in Python, with extensive experience using statistical and ML libraries such as NumPy, Scikit-learn, XGBoost, and PyTorch
Hands-on experience with workflow orchestration tools (e.g., Dagster) and container orchestration platforms (e.g., Kubernetes)
Proficiency in SQL and exposure to modern in-process databases like DuckDB for efficient analytics and distributed computing
Solid understanding of statistical validation methods, experimental design, hypothesis testing, and rigorous model evaluation metrics
Excellent analytical thinking skills, combined with the ability to clearly communicate data-driven insights and recommen