Senior Data Scientist
The ideal candidate has experience blending various datasets, building statistical/machine learning models, and communicating the results back to business partners.
Essential Duties and Responsibilities
- Apply machine learning, advanced analytical techniques, and critical thinking to solve complex business problems
- Seek, build, and consolidate data inputs proactively to create and improve models
- Translate business partners’ needs into data science projects and make technical decisions based on the tradeoff between complexity and value
- Drive projects, individually and collaboratively, through the data science lifecycle including data collection, exploration/analysis, model development, validation, and deployment
- Develop presentations to communicate key messages to senior sponsors, non-technical partners, fellow data scientists, and other stakeholders
- Work with technical partners to deploy models and streamline the process to scale our work
- B.S. degree in a quantitative field (e.g., Statistics, Engineering, Computer Science, Mathematics, Economics, Operations Research).
- 5+ years’ experience in data science building and deploying models into production
- Programming: compose clean, efficient, and reusable code in Python and/or R.
- Machine Learning / Modeling: understand what types of algorithms to use, their benefits and drawbacks, and how to implement them.
- Data Wrangling: write efficient SQL queries from scratch that blend data from a variety of sources in the right form for modeling, analysis, and scoring.
- Statistical Analysis: investigate trends, patterns, and relationships using statistical methods and tests to reach actionable conclusions and understand causal relationships.
- Data visualization: grasp data-ink ratios and bring data-driven stories to life through visuals.
- Curious: bring intellectual curiosity, an inquisitive nature, and a desire to deepen your knowledge and continue learning.
- Ownership: take responsibility to proactively advance projects, contribute to the team/organization, and improve existing processes.
- Business acumen: understand the bigger picture for customers and the business while connecting your work to key objectives for both.
- Storytelling: write and present messages and insights clearly to persuade non-technical partners that they can confidently use our models and solutions.
Bonus points if you have:
- A graduate degree (Masters or PhD) in a quantitative field
- Experience with spatial statistics, greenhouse gas emissions, or sensor data
- Proficiency using git for version control, collaboration, and releases
- Expertise with cloud services like AWS (SageMaker, S3, Redshift, etc.) and Snowflake
- Built dashboards for visualizing model outputs and performance monitoring using Microsoft PowerBI, Python dash, or similar tools
- provided by Dice