Senior Data Scientist - Hybrid, Minnesota

NMDP•Minneapolis, MN

1d•$105,000 - $140,000•Hybrid

About The Position

NMDP is seeking a Senior Data Scientist to join their Data Science & Insights team. This role will report to the Sr. Manager, Data Science & Insights and will be crucial in supporting the organization's five-year strategy. The position involves creating models for various operational processes, including model selection, feature selection, and accuracy measurement, while ensuring transparency and collaboration with internal partners. The Senior Data Scientist will provide input and analytical expertise to teams and leaders, mentor and train other data scientists, and drive the adoption of analytical approaches. This role requires the ability to influence business stakeholders and peers through analytical insights and transparency, working directly with cross-functional teams to share insights and provide accessible statistical output. Engagement with stakeholders will involve reviewing analysis and recommending additional data points and analytical techniques.

Requirements

Proficiency in Python and SQL.
Experience operationalizing statistical and machine learning models in a production environment.
Experience writing SQL queries with large datasets and complex, integrated systems.
Experience in designing and implementing data applications, including deploying them to production environments.
Expertise in data science, machine learning, data mining, operations research, and statistical modeling techniques, specifically for high-volume and complex datasets.
Knowledge of best coding practices.
Ability to choose the right tool from the data science toolkit for the task at hand, ranging from exploratory data analysis (EDA) to automated data pipelines in production.
Ability to prioritize work effort across multiple projects and articulate when a project is reaching the point of diminishing returns.
Ability to get up to speed and continue development on existing data science projects.
Ability to keep up with advancements in ML/AI and incorporate them to improve processes and the ability to generate insights.
Ability to collaborate with other data teams, including Business Intelligence, Data Governance, and Data Engineering.
Ability to display data visually, creating powerful presentations and stories which effectively quantify and justify value created to both analytical and non-analytical audiences.
Ability to lead and speak courageously, challenge ideas and propose alternatives.
Ability to grasp the diversity of systems and network and the ability to make recommendations and implement solutions.
Strong attention to detail and excellent organizational skills.
Well-developed interpersonal skills dealing with people of different cultures and styles.
Advanced communication skills to include writing and oral abilities to connect with a wide range of people.
Demonstrate excellent analytical, problem-solving with a strong customer focus.
Manage multiple assignments simultaneously with minimal direction or supervision.
Work and deliver under tight timeframes.
Bachelor’s Degree in Computer Science, Economics, Statistics, Mathematics, Physics, Finance, or other quantitative disciplines.
A minimum of six years professional hands-on experience translating analytics including statistical modeling, data mining, and predictive modeling/forecasting into actionable insights.

Nice To Haves

Master’s degree or higher.
Experience troubleshooting and resolving code errors.
Familiarity with bash or similar command line shells.
Familiarity with R.

Responsibilities

Independently identify, analyze, and interpret patterns in data using appropriate techniques, from simple aggregation to state-of-the-art machine learning (ML) methods.
Understand business processes and data to develop custom models that inform business decisions.
Ensure models are explainable to build confidence in results and stakeholder trust, leveraging statistical methods and advancements in explainable ML/AI.
Lead the development of processes and tools to monitor and analyze model performance and data accuracy.
Collaborate with stakeholders and data partners to define data requirements, translate insights, and facilitate decision-making.
Act as a source of unbiased data-supported insights, confidently challenging norms when novel insights are discovered.
Lead mentorship and support for data scientists.
Lead project subject matter experts in evaluating current business processes and identifying weaknesses or inefficiencies.