Staff Data Scientist
The position is responsible for developing data-driven solutions that support business and product decisions
It applies statistical modeling, machine learning, and data analysis techniques to extract insights, build predictive models, and develop tools that enhance internal decision-making and customer-facing capabilities
It applies statistical modeling, machine learning, and data analysis techniques to extract insights, build predictive models, and develop tools that enhance internal decision-making and customer-facing capabilities
Essential Duties & Responsibilities
Collaborate with cross-functional teams to define analytical questions and translate them into data science problems
Collect, clean, and prepare large and complex datasets for analysis, often including unstructured text and associated metadata
Build and evaluate models to solve a variety of problems such as classification, clustering, forecasting, or user behavior analysis
Develop and implement methodologies to measure model and system performance using appropriate statistical or behavioral metrics
Communicate findings and recommendations to technical and non-technical stakeholders in clear and actionable terms
Support the deployment, maintenance, and ongoing refinement of data science solutions in production environments
Document methodologies, assumptions, and results to support reproducibility and team knowledge sharing
Stay informed about advancements in data science, AI, and statistical modeling to apply emerging techniques as appropriate
Perform other duties and responsibilities as required to support business needs
Job Level Specifications
Mastery knowledge of industry best practices and disciplines
Considered a subject matter expert within the organization and contributes to the development of new concepts, techniques and standards
Considered a subject matter expert within the organization and contributes to the development of new concepts, techniques and standards
Develops solutions to highly complex and uniquely challenging situations
Assignments require extensive evaluation of alternatives and variables
Expected to make improvements to policies and procedures
Assignments require extensive evaluation of alternatives and variables
Expected to make improvements to policies and procedures
Works independently toward long-range goals and objectives
Assignments are often self-initiated using independent judgment and discretion
May act as informal team lead and/or coach less experienced team
Assignments are often self-initiated using independent judgment and discretion
May act as informal team lead and/or coach less experienced team
Serves as consultant to management and/or internal/external spokesperson for the organization on major initiatives related to policies, plans and long-range objectives
Actions may impact on the organization and its reputation
Effects of erroneous decisions may be long-lasting, influence the future course of the organization and/or require the expenditure of extensive additional resources
Effects of erroneous decisions may be long-lasting, influence the future course of the organization and/or require the expenditure of extensive additional resources
Minimum Qualifications
Education: University degree preferred
Typical experience: 8+ years in related role or experience
Proficiency in Python or similar programming languages used in data science
Strong foundation in statistics, machine learning, or a related quantitative field
Ability to work iteratively and experimentally to develop solutions in an agile environment
Familiarity with communicating technical findings to varied audiences
Actual experience may vary depending on role complexity, geography, and internal development opportunities or a comparable mix of training, education, and experience
Preferred Qualifications
Experience working in a collaborative data science team
Knowledge of standard libraries and tools such as Pandas, NumPy, SciPy, scikit-learn, or SpaCy
Exposure to big data tools (eg, Spark, Dask, MapReduce) and distributed computing
Familiarity with cloud environments (eg, AWS EC2, S3) and Linux-based workflows
Experience with version control tools (eg, Git) and lightweight application prototyping frameworks (eg, Dash, Shiny)
