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

Other Jobs To Apply

**TITLE:** Remote Data Scientist jobs – Senior Machine Learning Engineer (Python, TensorFlow, AWS) – Full‑Time – $120K‑$150K – Raymore, Missouri Remote --- 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‑level Remote 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 bolthires. ### 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 (bolthires met after the first month). - **SLA compliance:** 98 % of drift alerts triggered within the 24‑hour window. - **Code quality:** < 5 % of PRs require re‑work after review (tracked via GitHub Checks). - **Team growth:** Mentor at least two junior scientists to become independent contributors within six months. ### 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.

Back to blog

Common Interview Questions And Answers

1. HOW DO YOU PLAN YOUR DAY?

This is what this question poses: When do you focus and start working seriously? What are the hours you work optimally? Are you a night owl? A morning bird? Remote teams can be made up of people working on different shifts and around the world, so you won't necessarily be stuck in the 9-5 schedule if it's not for you...

2. HOW DO YOU USE THE DIFFERENT COMMUNICATION TOOLS IN DIFFERENT SITUATIONS?

When you're working on a remote team, there's no way to chat in the hallway between meetings or catch up on the latest project during an office carpool. Therefore, virtual communication will be absolutely essential to get your work done...

3. WHAT IS "WORKING REMOTE" REALLY FOR YOU?

Many people want to work remotely because of the flexibility it allows. You can work anywhere and at any time of the day...

4. WHAT DO YOU NEED IN YOUR PHYSICAL WORKSPACE TO SUCCEED IN YOUR WORK?

With this question, companies are looking to see what equipment they may need to provide you with and to verify how aware you are of what remote working could mean for you physically and logistically...

5. HOW DO YOU PROCESS INFORMATION?

Several years ago, I was working in a team to plan a big event. My supervisor made us all work as a team before the big day. One of our activities has been to find out how each of us processes information...

6. HOW DO YOU MANAGE THE CALENDAR AND THE PROGRAM? WHICH APPLICATIONS / SYSTEM DO YOU USE?

Or you may receive even more specific questions, such as: What's on your calendar? Do you plan blocks of time to do certain types of work? Do you have an open calendar that everyone can see?...

7. HOW DO YOU ORGANIZE FILES, LINKS, AND TABS ON YOUR COMPUTER?

Just like your schedule, how you track files and other information is very important. After all, everything is digital!...

8. HOW TO PRIORITIZE WORK?

The day I watched Marie Forleo's film separating the important from the urgent, my life changed. Not all remote jobs start fast, but most of them are...

9. HOW DO YOU PREPARE FOR A MEETING AND PREPARE A MEETING? WHAT DO YOU SEE HAPPENING DURING THE MEETING?

Just as communication is essential when working remotely, so is organization. Because you won't have those opportunities in the elevator or a casual conversation in the lunchroom, you should take advantage of the little time you have in a video or phone conference...

10. HOW DO YOU USE TECHNOLOGY ON A DAILY BASIS, IN YOUR WORK AND FOR YOUR PLEASURE?

This is a great question because it shows your comfort level with technology, which is very important for a remote worker because you will be working with technology over time...