A model is only as good as the data feeding it, and most of the interesting work in machine learning happens before anyone trains anything — cleaning a messy dataset, catching a bias hiding in a feature, figuring out why the numbers don't quite match what the business expects. We're hiring a
Remote Machine Learning Data Analyst to own that work; salary is $105,586 per year, fully remote.
What you'd own
You're not just running models — you're building the foundation they sit on and making sense of what comes out the other end. That spans data prep, model development, and turning results into something stakeholders can actually use.
- Collect, clean, and organize datasets from multiple sources, catching inconsistencies before they become model problems
- Work with data engineers to keep pipelines and storage running smoothly
- Help design and build machine learning models, including feature engineering to improve performance
- Train, test, and validate models for accuracy and reliability
- Analyze results to surface patterns that actually inform a decision, not just confirm what was already assumed
- Build dashboards and visualizations that make findings usable for people who don't work in Python all day
- Refine algorithms over time and keep an eye on what's changing in the field
There's also a project management side to this — juggling multiple initiatives at once, keeping stakeholders updated, and flagging problems before they derail a timeline. And a quieter but real responsibility: watching for bias in models and being the person who raises it before it ships.
What you'll need
Minimum education is a
Bachelor's degree in data science, computer science, statistics, or a related field — an advanced degree is a plus but not required. Beyond that, we're looking for at least
3 years of experience in data analysis or machine learning work.
- Proficiency in Python or R, with hands-on experience in libraries like TensorFlow, PyTorch, or scikit-learn
- Solid grounding in statistical methods, data preprocessing, and feature engineering
- Experience with visualization tools such as Tableau, Power BI, or Matplotlib
- Comfortable working independently in a remote setting without needing constant check-ins
Good to have
- Familiarity with cloud platforms — AWS, Google Cloud, or Azure — for ML workflows
- Experience mentoring less experienced analysts on ML concepts and tools
- Background presenting technical findings to non-technical stakeholders
How the work actually flows
Some weeks lean heavily into data cleaning and prep, which sounds tedious until you realize how much of a model's eventual accuracy traces back to that stage. Other weeks are spent deep in model tuning, or in meetings translating what a model actually found into something a non-technical stakeholder can use to make a call. This role sits at Naukri Mitra, a recruitment firm that places analysts into organizations actively building out their machine learning capacity — so the projects you land on tend to be genuinely new, not maintenance work on something built years ago.
Pay and benefits
- $105,586 annual salary
- Comprehensive health coverage — medical, dental, and vision
- Flexible working hours in a remote environment
- Funded professional development, including certifications and training
- Generous paid time off, covering vacation, personal days, and holidays
What actually matters here
Ethical AI practice isn't a line item — it's part of the job. That means checking models for bias before they go live, being straightforward about what a model can and can't reliably predict, and handling sensitive data with real care rather than just meeting the minimum compliance bar. If that sounds like extra work rather than the job itself, this probably isn't the right fit.
Who tends to thrive in this role?
People who get genuinely curious when a dataset doesn't behave the way they expected, instead of just forcing a fix and moving on. People who can explain a confusion matrix to someone who's never heard the term without making them feel talked down to. Self-direction matters a lot here — nobody's going to hand you a task list every morning.
Applying
Send your resume along with a short note on a machine learning project you worked on where the data surprised you — what you found, and what you did with it. That tells us more than a list of libraries you've used.
