Data Scientist 2

Data Scientist – Machine Data Insights Pay: 35-38 Are you passionate about using data to uncover insights that drive real-world impact? Our Machine Data Insights team is looking for a motivated Data Scientist to support product development analytics, aftermarket analytics, demand forecasting, and other strategic initiatives. This role helps us understand how customers use their equipment so we can improve safety, productivity, and business performance across the enterprise. About The Role As a Data Scientist, you’ll join cross-functional project teams to analyze complex machine and aftermarket datasets, improve data quality, and develop models that guide actionable decisions. You’ll move from primarily supporting others to contributing independently, applying advanced analytics and machine learning techniques to meaningful business challenges. You will work closely with global team members (primarily in North America) and support analysts and technical stakeholders who rely on accurate insights to make key decisions. What You’ll Do • Analyze and interpret machine usage and aftermarket sales data • Validate data quality and develop solutions to improve data pipelines • Build, refine, and validate statistical and machine learning models • Create dashboards and visualizations that support field teams and leadership • Collaborate with global team members to deliver insights and recommendations • Present analytical findings clearly to both technical and non-technical audiences What You Bring • 3–5 years of professional experience in data science or analytics • Bachelor’s degree in Computer Science, Statistics, Economics, Mathematics, or related quantitative field • Experience with cloud platforms (Azure, Snowflake strongly preferred) • Strong foundation in mathematics and statistical analysis • Ability to communicate insights clearly and build compelling PowerPoint summaries • Experience working with machine data or industrial equipment (preferred) • Proficiency in Python (NumPy, SciPy, pandas, etc.) and modern analytics tools • Solid understanding of machine learning techniques (e.g., clustering, logistic regression, CART, Random Forests, SVMs, neural networks) Top Skills • Cloud technologies (Azure, Snowflake) • Mathematics & statistical modeling • Communication & cross-functional collaboration #