ML Data Engineer

About Smart Working At Smart Working, we believe your job should not only look right on paper but also feel right every day. This isn’t just another remote opportunity - it’s about finding where you truly belong, no matter where you are. From day one, you’re welcomed into a genuine community that values your growth and well‑being. Our mission is simple: to break down geographic barriers and connect skilled professionals with outstanding global teams and products for full‑time, long‑term roles. We help you discover meaningful work with teams that invest in your success, where you’re empowered to grow personally and professionally. Join one of the highest‑rated workplaces on Glassdoor and experience what it means to thrive in a truly remote‑first world. About the role We’re looking for a hands‑on ML Data Engineer to join our growing machine learning team. This role sits at the intersection of data analysis, machine learning operations (MLOps), and client insight delivery — combining technical depth with analytical storytelling. You’ll work end‑to‑end across the ML lifecycle: from data collection and cleaning, through model monitoring and deployment, to developing analytical dashboards that drive meaningful client insights. If you thrive on blending analysis, engineering, and communication, this role offers the perfect balance of impact and autonomy. As a long‑term member of our ML team, you’ll take ownership of end‑to‑end data workflows — ensuring clean, reliable, and well‑structured data supports both analytical insights and production ML models. This role is ideal for someone who combines strong data engineering discipline with analytical reasoning, continuously improving model performance and data‑driven decision‑making. Responsibilities Insight & Research Conduct hypothesis‑led analysis on large conversational datasets to uncover trends, outcome drivers, and client‑ready narratives. Build and maintain benchmark datasets powering analytics dashboards and model evaluations, ensuring clear definitions and version control. Develop and maintain Power BI dashboards and automated reporting pipelines that deliver actionable insights to internal and external stakeholders. Translate raw conversational and model data into clear, evidence‑based recommendations. ML Ops Lifecycle (Operate & Improve) Own ML performance monitoring — managing experiment tracking, model registries, and performance dashboards. Track model drift, bias, and degradation; design and execute improvement experiments. Manage training data pipelines, feature store freshness, and ensure full data lineage and reproducibility. Partner with engineers to define automated evaluation frameworks, including bias detection, acceptance thresholds, and rollback procedures. Product & Data Engineering Collaborate with product teams to integrate analytical outputs and dashboards directly into the platform’s portal and APIs. Work closely with data engineering teams to design scalable ingestion, storage, and transformation pipelines across PostgreSQL, NoSQL, and data lake architectures. Support internal Copilot and automation features that rely on trusted, version‑controlled metrics and insights. Client Analysis & Delivery Run client‑specific analytics projects, from data exploration to narrative presentation. Communicate findings clearly to both technical and non‑technical stakeholders. Ensure analytical outputs align with business objectives and client strategy. Requirements 5+ years of professional experience in data analysis, ML analytics, or MLOps. 5+ years of proficiency in Python (pandas, NumPy) — including data cleaning, outlier removal, and analytical exploration. 4+ years of hands‑on experience with MLOps tooling, including feature stores, experiment tracking, model registries, and performance monitoring. 4+ years of working familiarity with LLM concepts and evaluation methods, with the ability to apply them in data‑driven model improvement. 4+ years of experience working with relational and NoSQL databases such as PostgreSQL and DynamoDB (or equivalent). 2+ years of experience with Power BI, Power Query (M), and DAX for data visualization and reporting; advanced Excel (pivots, complex formulas) is an acceptable substitute. 2+ years of experience in data warehousing and ETL processes for scalable data ingestion and transformation. 2+ years of experience with cloud data services (AWS, Azure, or GCP), particularly for managing ML and analytics workflows. Excellent communication skills and a structured, analytical approach to problem‑solving. Strong balance between applied ML expertise and deep analytical reasoning — demonstrating hands‑on data analysis, statistical validation, and the ability to connect insights to model performance and business outcomes. Nice to have Exposure to healthcare or life sciences datasets. BI/Analytics certifications (e.g., Microsoft Data Analyst, AWS Data Analytics). Hands‑on experience with LLMs or generative AI frameworks (OpenAI, Hugging Face, LangChain). Understanding of conversational AI data, including prompt optimization and model fine‑tuning techniques. Experience working across multi‑cloud environments or integrating data pipelines across AWS, Azure, and GCP. Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative field. Postgraduate education or certifications in ML/AI are advantageous. At Smart Working, you’ll never be just another remote hire. Be a Smart Worker — valued, empowered, and part of a culture that celebrates integrity, excellence, and ambition. If that sounds like your kind of place, we’d love to hear your story. #J-18808-Ljbffr