- Company Name
- Long View Systems
- Job Title
- Machine Learning Engineer/Data Scientist
- Job Description
-
Job title: Machine Learning Engineer/Data Scientist
Role Summary: Own the end‑to‑end analytics lifecycle, translating business goals into scalable machine learning solutions on Azure. Collaborate with engineers, architects, and stakeholders to design, develop, deploy, and monitor models while ensuring responsible AI and operational excellence.
Expectations: Deliver production‑ready models that drive business KPIs, maintain rigorous documentation, adhere to MLOps best practices, and actively engage clients through consulting, workshops, and industry events.
Key Responsibilities:
- Lead discovery and translate business objectives into analytical problems, KPIs, and success criteria.
- Design, build, and evaluate reproducible experiments (classification, regression, forecasting, NLP/LLMs) using Python, Azure ML, Databricks, and Spark.
- Engineer features, perform validation (cross‑validation, leakage checks), bias/variance analysis, and error breakdowns, applying Responsible AI principles.
- Partner with ML engineers to operationalize models via CI/CD pipelines, experiment tracking (MLflow), model registries, and real‑time/ batch evaluation.
- Architect ML platforms and data pipelines in Azure (Machine Learning, Databricks, Synapse/Microsoft Fabric, Data Lake Storage, Event/Service Bus).
- Produce clear design documents, runbooks, and handover materials for stakeholders and operations.
- Communicate model findings, trade‑offs, and visualizations to technical and non‑technical audiences, supporting decision making.
- Engage in client meetings, workshops, SOW estimation, and demos; maintain accurate time billing.
- Participate in audits, special programs, and industry events as a representative of the organization.
Required Skills:
- 5–6+ years of applied data science/analytics with production‑grade models impacting KPIs.
- Proficient in Python, statistical modeling, experiment design, tree‑based methods, GLMs, time‑series, and causal inference basics.
- Experience with Azure ML, Databricks/Spark, SQL, data lake/wrangling, and Azure Synapse/Fabric pipelines.
- Strong MLOps knowledge: MLflow, model lifecycle management, monitoring, alerts, data quality (Great Expectations style).
- Consulting experience, high‑level stakeholder facilitation, client engagement, and presentation.
- ITIL Incident Management familiarity, excellent problem‑solving, multitasking, and communication skills.
Required Education & Certifications:
- DP‑100, DP‑203, AI‑102, AZ‑900, AI‑900 (or equivalent).
- Certifications or experience in NLP/LLMs, Retrieval‑Augmented Generation on Azure.
- Advanced knowledge in probabilistic modeling, Bayesian methods, A/B testing, optimization, or relevant domain expertise (e.g., supply chain analytics, inventory optimization) is a plus.