Data Scientist

Ledelsea; Minneapolis (MN); Hybrid; USD 95,000 - 130,000; Full time

Job summary

Ledelsea is seeking an Intermediate Data Scientist to join Corporate IT in Arden Hills, MN. The role is hybrid, requiring in-office presence Tuesday through Thursday, with local candidates preferred.

The candidate will apply statistical methods and machine learning to drive revenue growth and cost reduction, partnering with business units and engineering teams to deliver measurable impact.

Qualifications

• Bachelor's degree in a data-related field.
• 3-5 years of hands-on experience applying statistics, ML, experiments, or decision science to business problems.
• Proficient in Python, R, and SQL with capability to review code and outputs.
• Solid knowledge of statistical inference, experiments, regression, classification, forecasting, feature selection, model validation, uncertainty, and monitoring.

Responsibilities

• Partner with stakeholders to frame use cases, decide success criteria, and align on decision needs.
• Profile, cleanse, transform, and integrate data while ensuring privacy and data quality.
• Translate business requirements into features, experiments, and analytical workflows.
• Develop and compare ML models with sound statistical reasoning and business context.
• Monitor model performance, explainability, and bias; document methods and results.
• Collaborate with product, engineering, and business teams for production integration.

Skills

Python

R

SQL

Statistical modeling

Education

Bachelor's degree in Data Science, Statistics, Computer Science, Engineering, or related field

Tools

Databricks

MLflow

Job description

Client is looking for an Intermediate Data Scientist to join Corporate IT. This role is based in Arden Hills, MN and requires hybrid attendance in the office Tuesday through Thursday (Open for local candidates only). This position is responsible for partnering with functions and lines of business to apply statistical methods, machine learning, and industry knowledge to key business decisions that support revenue growth and cost reduction.

With growing autonomy and guidance appropriate to project complexity, this position owns moderately complex analytical projects or defined components of larger initiatives, develops models and experiments, and translates findings into actionable recommendations. The role emphasizes statistical and machine learning expertise, business partnership, and industry knowledge rather than custom software development.

Responsibilities

• Partner with business stakeholders to frame use cases, understand industry drivers, and agree on decision needs and success criteria.
• Explore, profile, cleanse, transform, and integrate enterprise and reputable external data, verifying quality against source data, business rules, and industry context while following privacy, security, and sourcing requirements.
• Translate business requirements and domain knowledge into features, experiments, analytical workflows, and model evaluation criteria.
• Select, develop, compare, and refine statistical and machine learning models using sound statistical reasoning, business context, and reusable analytical components.
• Develop reproducible analytical workflows and evaluate and monitor model accuracy, bias, explainability, and performance drift against agreed business and technical criteria.
• Use approved AI tools to accelerate coding, testing, and documentation while remaining accountable for method selection, analytical quality, interpretation, and clear communication.
• Distinguish meaningful patterns from noise and communicate findings, assumptions, tradeoffs, uncertainty, risk, and quantified impact as clear business implications and practical recommendations.
• Support business owners in adopting recommendations and measuring results, while business owners retain accountability for adoption and operational decisions.
• Partner with product, engineering, platform, and business teams to plan incremental deliverables and support production integration, automated monitoring, or custom development when needed.
• Participate in analytical, model, and code reviews; document work and share statistical methods, domain insights, and reusable practices.

Required qualifications

• Bachelor’s degree in Data Science, Statistics, Computer Science, Engineering, or a related field.
• 3-5 years of hands‑on experience applying statistics, machine learning, experimentation, or decision science to business problems.
• Working knowledge of Python, R, and SQL, with experience using analytical platforms and model lifecycle tools such as Databricks and MLflow. Ability to review and validate code and analytical outputs, including AI-assisted work.
• Solid knowledge of statistical inference, experimental design, regression, classification, forecasting, feature selection, model validation, uncertainty, and model monitoring.
• Demonstrated ability to frame moderately complex problems, align success criteria with business owners, quantify impact, and translate technical findings into clear insights and actionable recommendations.
• Industry knowledge in agriculture or related commercial settings, with the ability to connect business drivers to analytical decisions.