Ambyint is a SaaS company that provides an Industrial IoT platform, leveraging physics-influenced AI models, plus closed-loop control to enable autonomous operations. As a result, we are an instrumental part of the climate technology evolution and a market leader in production optimization for the energy industry. We deliver step-change improvements to our customers by combining advanced physics, subject matter expertise, and data-informed insights with AI to automate operations and production optimization workflows. We’re coming off a strong year and are setting even higher ambitions for the future. To help us reach the next level, we’re looking for a (Senior) Data Scientist, Industrial Optimization & Decision Modeling to join our team.
As a Data Scientist in this role, you will support our industrial optimization and decision-modeling work, with an initial focus on artificial lift optimization in oil and gas production. This role is not just about building predictive models; the core challenge is estimating the impact of operating changes from sparse, noisy, and sometimes confounded field data, then using that information to support safe and trustworthy recommendations.
We prioritize candidates with strong mathematical and statistical foundations. We are looking for an applied data scientist who can comfortably reason from first principles about uncertainty, probability, model assumptions, optimization, causal ambiguity, and data quality. The ideal candidate should be able to understand not only how to fit a model, but also why it works, when it fails, and whether its outputs are trustworthy enough to support critical field decisions.
WHAT YOU’LL DO:
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Develop Bayesian optimization workflows for artificial lift optimization across gas lift, plunger lift, rod lift, and hybrid lift systems.
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Build probabilistic surrogate models that estimate production response, uncertainty, and risk from sparse and noisy field data.
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Design constrained optimization policies that account for operational limits, safety constraints, trust regions, and field-approved action ranges.
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Model intervention-response data using before/after windows, event quality flags, counterfactual baselines, and uncertainty-aware targets.
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Develop contextual models that condition recommendations on current well state, lift regime, production trends, pressure behavior, and plunger-cycle performance.
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Evaluate model calibration, predictive uncertainty, out-of-sample generalization, and decision quality across wells and operating regimes.
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Help design field experiments and sequential learning workflows that balance exploration, exploitation, and operational risk.
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Build diagnostics for model performance, uncertainty calibration, coverage, residuals by well, response heterogeneity, and support distance.
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Collaborate with SMEs and operators to translate model outputs into practical recommendations, risk flags, and decision explanations.
QUALIFICATIONS:
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Strong academic or applied background in Statistics, Mathematics, Engineering, Physics, Operations Research, Econometrics, or another highly quantitative field.
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Solid understanding of machine learning fundamentals and strong Python programming skills (pandas, NumPy, scikit-learn).
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Experience working with messy real-world datasets, especially time-series, sensor, operational, or event-based data.
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Comfort working with SQL or structured data sources to extract and manipulate complex data.
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Proven experience evaluating models beyond simple accuracy metrics, including residual analysis, cross-validation, subgroup performance, calibration, and error analysis.
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Ability to reason from first principles about assumptions, noise, uncertainty, bias, and model failure modes.
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Strong communication skills with the ability to translate complex statistical outputs into practical concepts for both technical and non-technical stakeholders.
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Willingness and ability to learn new advanced modeling approaches (such as BoTorch/GPyTorch stack and Bayesian workflows).
WHAT SETS YOU APART
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The gratification of a job well done comes from the satisfaction of your ‘customers’—in this case, field operators and engineers trusting your models.
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You don't just import model APIs; you have a deep curiosity for why a model works, when it fails, and how to prove it's operationally safe.
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You possess a strong sense of uncertainty awareness and pragmatic judgment around whether a model output is genuinely useful in a physical environment.
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Continuous learning and improvement are part of your mantra; you are excited to bridge the gap between advanced statistics and real-world industrial machinery.
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You are curious, creative, biased for action, and love solving problems where data is messy and answers aren't obvious.
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You have a background or familiarity with time-series forecasting, anomaly detection, causal inference, or estimating the impact of operational interventions.
WHAT SETS US APART
Ambyint is a scale-up company, possessing all the exciting edginess of a start-up with solutions that are proven and advancing daily. We pride ourselves on the strength of our team and company, and the positive environmental impact we are driving. We live by a simple and effective set of values.
WHAT’S IN IT FOR YOU
The opportunity to make a difference in a cutting-edge technology company that is highly spirited and focused on being a leading contributor in the energy transition space. We offer an environment steeped in creating a rewarding professional experience and having some fun along the way. We work in a hybrid working environment with a diverse and talented team. We have a passion for taking on big challenges to create a wealth of opportunities and we will support your development and career goals. Additionally, we offer a competitive compensation and benefits package.
COMMITMENT TO DIVERSITY, EQUITY, & INCLUSION
At Ambyint, diversity, equity, and inclusion are at the core of who we are. Our commitment to these values is unwavering – across all of our work, teams, and interactions with customers. They are central to our mission and to our impact. The understanding that having varied perspectives and backgrounds helps generate richer ideas to solve the complex problems of a changing, and increasingly diverse world is paramount to who we are and how we work together as a team.
If you are a passionate professional that likes to work in a diverse team environment supporting an entrepreneurial culture with a focus on delivering exceptional value for our customers, we invite you to apply.
