Remote Data Scientist jobs – Senior Machine Learning Engineer (Python, TensorFlow, AWS) – Full‑Time – $120K‑$150K – Raymore, Missouri Remote

TITLE:Remote Data Scientist jobs –Senior Machine Learning Engineer (Python, TensorFlow, AWS) – Full‑Time – $120K‑$150K – Raymore, MissouriRemote --- We’re a ten‑year‑old SaaS company that started in a cramped garage in Raymore, Missouri and has since grown into a 200‑person organization serving more than 15,000 small‑business customers across North America. Our product – a real‑time inventory‑visibility platform – lives in the cloud, and the decisions our customers make every day depend on the predictions we generate.That’s why we’re looking for a senior‑levelRemote Data Scientist who can take ownership of the end‑to‑end machine‑learning pipeline, from raw data ingestion to production‑grade model monitoring. The role is remote, but the team still meets once a week on a video call that we all jokingly call “the coffee‑break stand‑up.” ### Why this role exists now In the last twelve months we added two new data sources: a POS‑stream from a major grocery chain and a fleet of IoT sensors on delivery trucks. Those streams increased our daily data volume by 68 % and opened a new line of business we’re calling “Predictive Re‑stock.” To turn those streams into actionable insights we need a data scientist who can design, validate, and ship models that run on both AWS and GCP.Our current team of six data engineers and two junior scientists has built a solid feature store, but we lack a senior person who can set technical standards, mentor the junior members, and embed robust governance into the model lifecycle. We’ve also committed to a new Service Level Agreement (SLA) with a marquee client – 95 % model‑drift detection within 24 hours – and we need your expertise to meet that target. ### What you’ll spend your day doing | Time | Activity | |------|----------| | 20 % | Data exploration & cleansing – write Jupyter notebooks in Python and R to profile the new POS and sensor data, flag anomalies, and document findings in Confluence.| | 20 % | Feature engineering – design time‑series features using pandas, dask, and Spark, store them in our Snowflake data warehouse, and push them to the feature store managed by Feast. | | 20 % | Model development – prototype with scikit‑learn, XGBoost, and TensorFlow; run hyper‑parameter sweeps on Vertex AI (GCP) or Sage‑Maker (AWS). | | 15 % | Productionization – containerize models with Docker, orchestrate pipelines in Airflow, and deploy to Kubernetes clusters that auto‑scale based on traffic.| | 15 % | Monitoring & governance – set up Prometheus alerts, Grafana dashboards, and drift detection using Evidently AI; write post‑mortems that feed back into the data catalog. | | 10 % | Mentorship & collaboration – pair‑program with junior scientists, review pull requests on GitHub, and run fortnightly brown‑bag sessions on emerging ML research. | * Note:* All work is done remotely, but we rely on a strong culture of async communication. You’ll use Slack for quick questions, Notion for project roadmaps, and our internal wiki for knowledge sharing.###The metrics that matter - Model accuracy: Lift > 12 % over baseline for Predictive Re‑stock forecasts. - Latency: 95 % of inference calls return under 150 ms (target met after the first month). - SLA compliance: 98 % of drift alerts triggered within the 24‑hour window. - Code quality: The tech stack (8‑12 tools we love) 1.Python 3.11 – our primary language for modelling, data wrangling, and API glue. 2. R – used by the analytics team for exploratory statistics on A/B tests. 3. SQL (Snowflake + PostgreSQL) – for ad‑hoc queries and data‑warehouse maintenance. 4. Apache Spark – distributed processing of the sensor streams. 5. TensorFlow & PyTorch – deep‑learning frameworks for demand‑forecast models. 6. scikit‑learn & XGBoost – classic ML algorithms for classification tasks. 7. AWS SageMaker & GCP Vertex AI – managed training and deployment services.8. Docker & Kubernetes (EKS & GKE) – containerization and orchestration of production workloads. 9. Airflow – DAG‑based pipeline orchestration for ETL and model‑training jobs. 10. Feast (Feature Store) – central repository for feature versioning and serving. 11. Prometheus + Grafana – monitoring stack for model latency and drift. 12. Evidently AI – automated reporting of data‑drift, model‑performance, and fairness metrics. We also keep an eye on MLflow for experiment tracking, DVC for data versioning, and Looker for dashboarding, but the twelve tools above are the daily workhorses.### Who you are -Experience: 5 + years building production‑grade ML models, preferably in a SaaS or e‑commerce environment. You have shipped at least three end‑to‑end pipelines that survived a full production lifecycle. - Statistical chops: Comfortable with hypothesis testing, Bayesian inference, and time‑series analysis. You can explain why a p‑value of 0.04 matters to a product manager who isn’t a statistician. - Software engineering mindset: You write clean, testable code, follow CI/CD best practices, and understand version control at a deep level (Git branching, code reviews, automated linting).- Cloud fluency: Hands‑on experience with either AWS or GCP (or both) in a data‑engineering context – you know IAM, VPC, and cost‑optimization strategies. - Communication: You can turn a 30‑minute technical deep‑dive into a story that a non‑technical stakeholder can act on. - Mentorship: You’ve led code‑review sessions, paired with junior teammates, or taught a workshop on model interpretability. Nice‑to‑have: Experience with reinforcement learning, knowledge graphs, or building recommendation systems.Familiarity with privacy‑preserving techniques (differential privacy, federated learning) is a bonus but not required. ###The human side of the job We believe data science isn’t just about numbers; it’s about people. One of our senior engineers, Maya, recently told us: > “I was on a call with a customer support rep who was getting frustrated because a model kept flagging false positives. We walked through the feature importances together, discovered a data‑quality issue, and fixed it in under two hours.Seeing that relief on her face reminded me why I love this work.” That moment is why we’ll pair you with a “customer‑voice” champion – a product manager who spends a day a week listening to support tickets so you always know the real‑world impact of your models. ### What we offer (remote, but not remote‑only) - Competitive compensation: Base salary $120K‑$150K, plus quarterly performance bonuses tied to model SLA adherence. - Equity: 0.025 %–0.05 % RSUs that vest over four years. -Benefits: Health, dental, vision, and a $1,200 annual wellness stipend.-Professional development: $2,500 per year for conferences (NeurIPS, KDD, etc.) and unlimited access to online courses (Coursera, Udacity). - Work‑from‑anywhere policy: As long as your internet connection meets 25 Mbps download and you’re in a time zone that overlaps at least 4 hours with our core hours (8 am‑12 pm PT). - Team culture: Quarterly “virtual coffee‑climb” where we share non‑work stories, a digital “game‑room” for after‑hours trivia, and a bi‑annual in‑person retreat in Raymore, Missouri (the last one was a weekend in the mountains that ended with a surprise snowball fight).### How we hire – a transparent process 1. Resume & brief cover letter – tell us why you’re excited about remote data science in Raymore, Missouri and which project in your portfolio best demonstrates end‑to‑end model deployment. 2. Screening call (30 min) – with our Lead Recruiter. Expect a casual chat about your background and a quick “two‑sentence” pitch of your most proud ML project. 3. Technical interview (90 min) – a live coding session on a shared Jupyter notebook. We’ll ask you to explore a synthetic dataset, engineer a feature, and train a simple model.No trick questions – we care about your thought process. 4. System design interview (60 min) – with the Head of Data & Analytics. You’ll design a production pipeline for a new data source, explaining choices around storage, orchestration, monitoring, and cost. 5. Leadership & culture interview (45 min) – with the VP of Product. Topics include mentorship style, handling stakeholder disagreements, and how you keep up with ML research. 6. Final chat (30 min) – a casual “meet the team” video call where you’ll meet your future teammates, ask any lingering questions, and get a feel for the day‑to‑day vibe.We aim to complete the process within three weeks, and we’ll give you detailed feedback after each step. ###Your next steps If you’ve read this far, you’re probably already picturing yourself building a feature‑store backed demand‑forecast model that slashes stock‑outs for our customers. Click “Apply” and attach a short (max 2 pages) portfolio that includes: - A brief description of each project (objective, data, impact). - Links to public GitHub repos or notebooks (private repos are fine; we’ll request access).- Any relevant metrics (e.g., AUC‑ROC improvement, latency reduction). Don’t forget to mention Raymore, Missouri in your cover letter – we love seeing a personal connection to the region, even if you’ll be working remotely. We’re excited to learn how you’ll help us turn raw data into reliable, real‑time insights for thousands of businesses. Let’s build something that matters, together. Apply tot his job