Senior Data Scientist

Vikta Energy is building the largest independent Tesla Supercharging network in the world. Through our relationship with Tesla, we are deploying and operating high-performance Supercharging infrastructure at leading retail, convenience, and commercial destinations, combining disciplined capital investment with data-driven site selection to create a network designed for scale, reliability, and strong site-level economics.

Our ambition goes well beyond charging infrastructure. Vikta Energy is developing an intelligent technology platform that brings together advanced data science, predictive site intelligence, network performance, and tools that help our host partners connect charging activity to business value. Our mission is to “Build the world’s most intelligent energy infrastructure network, connecting people, businesses and energy to create lasting value.”

Headquartered in Austin, Texas, Vikta Energy is bringing together exceptional builders, technologists, and operators to pursue that mission and shape the future of energy infrastructure.

Reporting directly to the SVP of Data Science and Decision Intelligence, you’ll be one of the first members of the data science team. You’ll work on building Vikta’s data science function from the ground up. You’ll own modeling, pipelines, and analytics end-to-end, working on key site-selection and utilization decisions. This is a high-autonomy, high-ambiguity role suited to someone who’s comfortable owning a problem from messy data through to a production prediction and a stakeholder-ready insight.

• ML modeling. Develop, validate, and maintain ML models, starting with the EV charger utilization model and expanding to others as the roadmap grows.

• Feature engineering. Source, clean, and engineer features from various datasets.

• Data pipelines. Build and maintain reproducible ingestion and transformation pipelines that feed both models and reporting, with attention to data quality and lineage.

• Dashboards & BI. Build interactive dashboards and reporting that make model outputs and business metrics legible to non-technical stakeholders.

• Analytics support. Serve as an analytics expert to the rest of the company. Provide ad hoc analysis, decision support, and turning open-ended business questions into tractable data work.

Required:

• 6+ years building predictive models that were used in production. Ownership from problem framing through deployment, monitoring, and retirement.

• Production experience with gradient-boosted trees (LightGBM, XGBoost). Can explain how they work, how to improve them, their limitations, etc.

• Spatial data fluency. geopandas, PostGIS, H3 or equivalent. Understands spatial autocorrelation and why random k-fold leaks on geographically distributed targets.

• Validation discipline. Designing holdouts that mirror deployment conditions, calibration, uncertainty quantification, and the judgment to know when a metric gain is noise.

• Inference from observational data. Small samples, no randomization, confounded selection. Comfortable reasoning about bias and running sensitivity analysis. This is not an A/B testing role.

• Strong Python (pandas, Num Py, scikit-learn) and SQL beyond basic joins. Window functions, query tuning against a warehouse.

• Feature engineering from messy third-party data. Reconciling inconsistent geographies, identifiers, and vintages across sources.

• Reproducible workflows. Git-based development, versioned data and experiments (DVC, MLflow, or equivalent).

• Can derive, defend, and debug your work independently. AI tooling is expected and encouraged for speed, but you will be asked to justify modeling choices from first principles in review, and to diagnose why a model is wrong.

• Ra…