Operate and adapt Machine Learning pipelines, such as regression and random forest, to ensure data accuracy and consistency (20%). Build novel Machine learning pipelines and workflows, such as Spark/Scala, and work with leadership and stakeholders to test and validate data outputs (15%). Develop scalable processes for automating reports and visualizations to communicate findings to stakeholders (5%). Leverage existing insights and formulate novel strategies to provide actionable recommendations to stakeholders in data science, engineering, and product teams (10%). Collaborate with cross-functional teams to identify new opportunities requiring the use of modern data science and Machine Learning techniques (10%). Apply mathematical and statistical theories and techniques to solve practical problems with respect to our company's business issues (5%). Design and implement novel models and experiments using cutting edge analytical, mathematical, and machine learning methods, such as LLM and deep learning, to drive growth in new company avenues (5%). Document data sources and processes for data analysis and modeling (5%). Deliver timely and precise responses to internal and external inquiries about our datasets and models (10%). Utilize technical expertise to process complex datasets, establish quality control procedures, and derive actionable insights through analytics (5%). Use SQL to conduct end-to end datasets across our business to extract insights, trends, and patterns utilizing mathematical, statistical and probability methodologies (10%).
Minimum Requirements: Master’s degree in Applied Statistics, Mathematics, Physics, Computer Science, or a related technical field or foreign degree equivalent and 2 years of data analysis experience, including 2 years of experience in each of the following:
- Large-Scale Data Extraction and Processing Using SQL and Apache Spark
- Causal Inference Analysis Using SQL and Python
- Predictive Modeling Using Linear Regression in Python Machine Learning and Deep Learning modeling for Customer Feedback Analysis
- Data Pipeline Automation and ETL Development with Python, Scala, Spark and Airflow
- Utilizing Data Visualization Tools and Business Monitoring Using AWS Quicksight
- Leveraging Git for version control to maintain reproducible data science workflows, managing code changes across branches, and supporting collaborative development of machine learning models and data processing pipelines.
- Position also requires completion of graduate-level coursework in implementing machine learning, Deep Learning and Natural Language Processingtechniques in Python and R to provide data-driven assessments to support customer feedback analysis; and data visualization and predictivemodeling using linear regression, building and validating linear regression models in Python to provide interpretable business insights. Experience may be gained concurrently.
Position full-time, 40 hours per week, with Fuelcomm Inc. d.b.a. Stackline Inc, located in Seattle, WA.
Apply by emailing resume to cbauer@stackline.com (mailto:cbauer@stackline.com). Must reference Job#: 09302026
SALARY: Salary range of $130,000 to $160,000 per year. BENEFITS: Stackline offers a full range of benefits, including medical, dental and vision plans, 401(k), comprehensive paid time off, FSA, disabilityinsurance, life insurance, EAP services, and others.