Qualifications
- MS or PhD in Mathematics, Computer Science, Engineering, or Economics (required).
- 3–6 years’ experience in Big Data environments, specifically Hive/Spark, with deployed scalable models on high‑volume data sets (1TB+).
- Expertise in building and deploying recommender systems and enhancing site search at scale.
- Proficiency in SQL and Python/Spark/R for deploying machine‑learning products.
- Strong knowledge of machine‑learning techniques, including GLM regression, k‑means/ hierarchical clustering, PCA, decision‑tree ensembles (RF, GBM, XGBoost, neural networks), Bayesian statistics, time‑series analysis, deep‑learning recommendations, collaborative filtering, and content filtering.
- Ability to debug algorithms and models with massive structured or unstructured data sets.
- Experience in telecom, retail, e‑commerce, consumer packaged goods, mobile, or consumer electronics industries.
- Hands‑on Agile experience (Scrum/Kanban) and familiarity with JIRA and Confluence.
- Solid understanding of Hadoop (Hive, Impala) and Spark (PySpark/Scala), as well as Python/Scala/R/Java and SQL.
- Excellent analytical, problem‑solving, and communication skills.
- Detail‑oriented, proactive, and capable of independent contribution while valuing team feedback.
- Comprehensive awareness of coding standards, documentation, testing, and quality compliance.
Responsibilities
- Design, develop, and deploy scalable, reliable machine‑learning models and data products.
- Ensure rigorous testing, documentation, and overall model quality.
- Analyze, clean, and visualize terabytes of data; build efficient data pipelines.
- Collaborate with product managers, data engineers, and leadership to source data, clarify requirements, and deliver comprehensive solutions.
- Communicate findings, progress, and risks to stakeholders in a clear, concise, and timely manner.
- Adhere to agile principles, using JIRA and Confluence for project planning and tracking.
- Document modeling work, maintain code quality, and follow team standards.
- Identify and implement automation opportunities within data pipelines wherever feasible.
Benefits
Regular full‑time employees (salaried or hourly) have access to medical, dental, vision, life insurance, 401(k), employee purchase program, tuition assistance (after six months), paid time off, student loan program (after six months), wellness incentives, and additional benefits. Employees are also eligible for MBO bonus compensation based on company, division, and individual performance.
#J-18808-Ljbffr