> Markdown version of [/jobs/ext/3049598-sr-data-scientist](https://www.wearedevelopers.com/jobs/ext/3049598-sr-data-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr. Data Scientist - **Company:** Mariana Minerals - **Location:** Ann Arbor, MI, United States - **Experience:** Expert - **Salary:** $140,000.0 - $173,000.0 - **Contract:** Permanent contract - **Skills:** Business Analytics Applications, Data Analysis, Data Infrastructure, Statistical Hypothesis Testing, Python (Programming Language), Standard Sql, SciPy, Statistical Process Control (SPC), Large Language Models, Pandas, Statistics Packages, Data Analytics, Looker Analytics - **Published:** September 24, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=4f9d2c4ba5156b25 ## About the Role Must have * 3-6+ years in data science, statistics, analytics, or a quantitative research role where you owned analyses that drove real decisions * Deep applied statistics: experimental design, hypothesis testing, regression, uncertainty quantification - and the judgment to know which tool fits the question and which assumption you just violated * Strong SQL and Python (pandas, statsmodels, scipy) - enough to get your own data, run your own analysis, and produce your own reporting without waiting on someone else * Track record building reporting and dashboards people actually use, with the product sense to know what belongs on a dashboard versus in a memo * Ability to work with messy, real-world measurement data - missing values, inconsistent sampling, instrument error - and be clear about what it can and can't support * Exceptional written communication; much of this job is making a technical finding land with operators, engineers, and executives * Comfort being the statistical authority in the room Nice to have * Background in mining, metallurgy, chemicals, energy, manufacturing, or other heavy industry - especially metallurgical accounting, mass balance reconciliation, or sampling theory * Experience with statistical process control, or measurement system analysis * Experience with BI and analytics tooling (Hex, Looker, or embedded analytics) and an opinion about how to deploy them * Degree in statistics, chemical engineering, chemistry, operations research, economics, or a related quantitative field * Experience building an analytics function at a company where one didn't exist yet * Working fluency with LLM-assisted analysis and where it genuinely speeds up analytical work versus where it quietly introduces errors ## Description * Design and analyze plant trials and experiments - DOE, sample sizing, control selection, and the analysis that says whether a process change did what it was supposed to do and at what confidence. * Own the definitions of the metrics the business runs on: recovery, grade, throughput, yield, uptime, unit cost. Decide what each one means, make the definition consistent across teams, and defend it when someone wants to compute it differently. * Build and own the reporting and analytics layer - the recurring reporting, the dashboards, and the self-serve tooling that lets operators, engineers, and business leads answer their own questions. * Quantify uncertainty honestly: sampling error, assay variability, instrument drift, measurement system analysis. * Apply statistical process control and capability analysis to plant operations - know when a process has actually shifted versus when it's a normal excursion. * Run the deep-dive analyses that don't have a home: why last month's recovery dropped, what's driving cost variance, which of these three suppliers is actually better, whether this correlation is real. * Do forecasting and estimation for production, cost, and capacity planning - applied statistics that informs commitments, not research models. * Partner with the Technical Product Manager for Data & Analytics Platform on what the reporting and analytics stack needs next, and be a demanding internal customer of the data platform when the data isn't fit for purpose. * Raise the analytical bar across the company: review other people's analyses, catch the flawed comparison before it reaches a decision meeting, and teach the teams around you enough statistics to stop making the same mistake twice. * Write findings up so they're actually used - a clear memo a non-statistician can act on, not a notebook that needs you in the room., * Rigor with a deadline: You know the difference between the analysis worth another week and the one that's good enough to decide on today, and you say which is which. * Skeptical by default: You ask how the data was collected before you analyze it, and you're the person who notices the sensor was miscalibrated for three weeks. * Structure from ambiguity: Turn a vague business question into a well-posed statistical one, get the data you need, and own the answer end to end. * Explicit about confidence: Communicate uncertainty in a way that helps people decide, rather than hedging so heavily the analysis becomes useless. * Ecosystem fluency: Understand where your data comes from, what the models and simulators downstream do with it, and where the reporting layer sits relative to the platform underneath it. ## Related Videos - [How building an industry DBMS differs from building a research one](https://www.wearedevelopers.com/videos/768-how-building-an-industry-dbms-differs-from-building-a-research-one) - [Advanced Typing in TypeScript](https://www.wearedevelopers.com/videos/496-advanced-typing-in-typescript) - [Python Data Visualization @ Deepnote (w/ PyViz overview)](https://www.wearedevelopers.com/videos/113-python-data-visualization-deepnote-w-pyviz-overview) - [Data Science, ML & AI in the Oil and Gas Industry at NDT Global - Dr. Katja Träumner](https://www.wearedevelopers.com/videos/1308-data-science-ml-ai-in-the-oil-and-gas-industry-at-ndt-global-dr-katja-traumner) - [Data Science on Software Data](https://www.wearedevelopers.com/videos/162-data-science-on-software-data) - [Analytics in the Age of Agentic AI: A tour of ClickHouse and Langfuse](https://www.wearedevelopers.com/videos/100240-analytics-in-the-age-of-agentic-ai-a-tour-of-clickhouse-and-langfuse) ## Related Articles - [Data Analyst Salary in the UK](https://www.wearedevelopers.com/magazine/278-data-analyst-salary-in-the-uk) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Data Analyst Salary Austria](https://www.wearedevelopers.com/magazine/275-data-analyst-salary-austria)