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Mariana Minerals is a software-first, vertically integrated minerals company supplying critical minerals for modern energy, AI, and defense technologies. The company is reimagining the minerals supply chain by combining deep industry expertise with advanced software, automation, and data-driven decision-making.
You will own the statistical rigor and reporting infrastructure that makes Mariana's operational data trustworthy and actionable. This is not an ML modeling role or a data engineering role—your peers build the models and move the data. You bring statistical expertise and clear reporting to a company generating substantial operational data from sensors, assays, lab benches, and plant logs, ensuring that conclusions drawn from that data are sound.
Key responsibilities include:
- Design and analyze plant trials and experiments using DOE, sample sizing, control selection, and rigorous statistical analysis to determine whether process changes achieved their intended effects and at what confidence level.
- Own metric definitions across the business (recovery, grade, throughput, yield, uptime, unit cost), ensure consistency across teams, and defend definitions when questioned.
- Build and maintain the reporting and analytics layer—recurring reports, dashboards, and self-serve tools enabling operators, engineers, and business leaders to answer their own questions.
- Quantify uncertainty honestly, accounting for sampling error, assay variability, instrument drift, and measurement system limitations.
- Apply statistical process control and capability analysis to plant operations, distinguishing real process shifts from normal variation.
- Conduct deep-dive analyses addressing questions without a natural home: cost variance drivers, supplier comparisons, correlation validation, and production forecasting.
- Perform forecasting and estimation for production, cost, and capacity planning—applied statistics informing real commitments, not research models.
- Partner with the Technical Product Manager for Data & Analytics Platform to define the reporting and analytics stack roadmap, and serve as a demanding internal customer when data quality is insufficient.
- Raise the analytical bar across the company by reviewing analyses, catching flawed comparisons before they reach decision meetings, and teaching teams statistical fundamentals.
- Communicate findings clearly in memos and reports that non-statisticians can act on, rather than notebooks requiring your presence.
You will operate with rigor balanced against deadlines, distinguish analyses worth additional time from those ready to inform decisions today, and maintain healthy skepticism about data collection and quality. You excel at translating vague business questions into well-posed statistical problems, securing necessary data, and owning answers end-to-end. You communicate uncertainty in ways that enable decision-making rather than paralyzing it, and you understand the full data ecosystem—where data originates, how downstream models and simulators use it, and how the reporting layer sits relative to the platform.
REQUIREMENTS
Must have:
- 3–6+ years in data science, statistics, analytics, or quantitative research where you owned analyses driving real business decisions
- Deep applied statistics expertise: experimental design, hypothesis testing, regression, uncertainty quantification, and judgment about which tool fits which question and which assumptions you may have violated
- Strong SQL and Python (pandas, statsmodels, scipy)—sufficient to retrieve your own data, run your own analyses, and produce your own reporting without dependency on others
- Track record building reporting and dashboards that people actually use, with product sense to distinguish dashboard content from memo content
- Ability to work with messy, real-world measurement data (missing values, inconsistent sampling, instrument error) and clarity about what it can and cannot support
- Exceptional written communication; much of this role involves making technical findings land with operators, engineers, and executives
- Comfort serving as 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 opinions on deployment
- Degree in statistics, chemical engineering, chemistry, operations research, economics, or related quantitative field
- Experience building an analytics function at a company where one did not previously exist
- Working fluency with LLM-assisted analysis and understanding of where it genuinely accelerates work versus where it introduces errors
