Principal (or Senior Principal) Data Scientist
Locations : USA, Canada or Latin America (possibility of working remotely)
Roche is here to deliver better outcomes to more patients, faster.
If you are interested in helping us transform into a more diverse, transparent, inclusive, collaborative and agile team this role is for you.
Your mindset and behavior are as important to us as your experience and capabilities.
We ask you to bring your unique personality to the team so we can celebrate a more diverse and inclusive workforce.
As a People and Culture Data Scientist, your work will impact the way Genentech and Roche function throughout our candidate and employee lifecycle.
Whether it is finding and hiring the best talent to enabling and retaining employees, your work will impact every facet of our quest to cure and treat diseases for our patients and you will personally help shape and define this journey.
We are looking for a Principal or Senior Principal Data Scientist to work on our impactful and dynamic team. This Data Scientist will work closely with other Data Scientists, Engineers, and Product Owners to deliver products to our employees including the most senior leaders.
We use a modern technology stack that includes Python, SQL, and AWS. This Data Scientist will utilize deep learning, machine learning, natural language processing, and more to drive impact and shape our company culture.
Leadership of analytical projects and initiatives
Thought leadership on how to use advanced statistics and ML / AI in the HR domain
Synthesize requirements from non-technical stakeholders
Drive decision-making by clearly tell stories with data
Inspire and collaborate closely with team members (data scientist, engineers) and stakeholders across the company to drive success
Promote (both your own and others on the team) ideas and vision to external stakeholders and potential customers
Mentor other data scientists
Help team navigate landscape in a large, global organization
Masters, PhD, or equivalent work experience in a quantitative discipline (e.g., statistics, mathematics, physics, economics, operations research, engineering)
Significant experience leading data science / analytics projects in real-world setting
Significant experience with applying Python frameworks, libraries (e.g., Numpy, Scipy, Pandas, scikit-learn), and version control tools (git)
Significant experience with applying the following techniques (and a working knowledge of real-world advantages / drawbacks) :
Advanced statistical methods (regression, hypothesis testing, experimental design, etc.)
Machine learning (random forest, GBM, NLP, social network analysis, clustering, etc.)
Deep learning (using tensorflow, pytorch, or keras)
Hands-on experience in developing end-to-end analytic solutions (full data science life cycle), from ETL to model building, model deployment to model performance monitoring
Self-starter with extracting, storing, and analyzing structure / unstructured datasets
Preferred (or willingness to learn more of)
Experience applying appropriate feature engineering, cross-validation, and ML techniques for longitudinal / panel time-series datasets
Experience with applying AWS (e.g., S3, EC2, Sagemaker, Glue)
Experience in developing reusable functions and best practice data science processes to assist in scaling and operationalizing data science life cycle
Familiarity with agile processes (SCRUM, Kanban)
Familiarity with software development processes