Data Scientist

Aptive Pest Control•Provo, UT

2d•$100,000 - $130,000•Onsite

About The Position

We are seeking a hard-working, innovative, detail-oriented, and creative team player to join our Aptive team! This is a full-time Data Scientist position located in Provo, Utah . This role combines advanced statistical expertise with machine learning, analytics engineering, and business acumen to improve process efficiency, enhance forecasting accuracy, and guide strategic initiatives. What we offer: $100k to $130k Annually, depending on experience Annual Bonus Group Health, Dental, and Vision plans Pet insurance, Life insurance, and EAP benefits 401K with employer match up to 4% Paid holidays and paid time off Opportunity for advancement Upbeat and exciting company culture and much more!

Requirements

Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Economics, or a related quantitative field; or equivalent practical experience
3-5 years in statistical analysis, data science, or related analytical roles with proven experience working with large datasets in business environments
Advanced statistical and machine learning expertise, including causal inference, experiment design, model development, and mitigation of common data biases
Proficiency in Python, SQL, data pipeline architecture, cloud data tools (e.g., Snowflake, Airflow, AWS), and BI platforms such as Tableau, Sigma, or Domo
Strong ability to translate complex analytical concepts into clear, actionable insights for non-technical stakeholders, supported by exceptional communication and visualization skills
Demonstrated analytical rigor, attention to detail, and ability to manage multiple projects while collaborating effectively across cross-functional teams

Responsibilities

Develop and refine machine learning models to support forecasting, classification, ranking, or optimization needs across business units
Build, maintain, and enhance data pipelines, data models, and automated workflows to ensure reliable and scalable analytics operations
Conduct statistical and causal analyses to detect performance trends, identify root causes, and inform operational decision-making
Translate ambiguous business questions into analytical frameworks, clearly outlining hypotheses, assumptions, and limitations
Identify and correct data biases, including selection bias, survivorship bias, Simpson’s paradox, data leakage, and collider bias to ensure analytical integrity
Design, execute, and evaluate causal experiments and A/B tests to measure the impact of operational or product changes
Develop, monitor, and communicate KPIs that improve visibility into operational performance, resource allocation, and strategic outcomes
Prepare and present clear, decision-ready insights to executive leadership and cross-functional partners using visualizations, dashboards, and written summaries
Use SQL, Python, and internal data platforms (e.g., Snowflake, Airflow, AWS) to extract, validate, and transform large datasets for analysis and modeling
Collaborate with Engineering, Product, Operations, Finance, Marketing, and Strategy teams to align models, data structures, and insights with organizational needs
Validate analytical work rigorously, ensuring reproducibility, accuracy, and proper documentation across all models and deliverables
Provide guidance on analytical methods, experiment design, and statistical best practices to cross-functional stakeholders