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About This Role
Vikta Energy is building the world’s leading intelligent energy platform and the largest independent Tesla Supercharger network in North America. Vikta combines Tesla’s industry-leading charging technology with proprietary AI, machine learning, and software to identify the highest-value locations, maximize utilization, and connect charging customers with the businesses where they charge, creating new value for drivers, retailers, and property partners.
ABOUT VIKTA ENERGY
Vikta Energy is building the world’s leading intelligent energy platform and the largest independent Tesla Supercharger network in North America. Vikta combines Tesla’s industry-leading charging technology with proprietary AI, machine learning, and software to identify the highest-value locations, maximize utilization, and connect charging customers with the businesses where they charge, creating new value for drivers, retailers, and property partners.
ABOUT THE ROLE
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.
WHAT YOU’LL DO
- 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.
WHAT YOU’LL BRING
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, NumPy, 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.
- Raises the technical bar on people around you. Reviewing others' modeling work, setting conventions, making the team's output better.
- Translates model output into decisions executives and site development teams act on.
- Self-directed under ambiguity. Comfortable defining the problem and adjusting as the project continues.
Preferred / Nice to Have:
- EV charging, energy, mobility, or retail site selection / location analytics
- AWS, particularly lakehouse patterns
- Standing up data science infrastructure and workflows from scratch
- Satellite or aerial imagery analysis
- BI and dashboard development
COMPENSATION & BENEFITS
Compensation Range: $160,000 - $200,000 based on experience level, skills, and location.
- Competitive Health, Dental & Vision plans
- Top tier bonus program, where overachievement is rewarded
- Equity with real potential for growth
- Remote/Hybrid flexibility
ADDITIONAL INFORMATION
Work Authorization: Must be authorized to work in the U.S.
Compensation Range: $180K - $220K
Salary Context
This $160K-$220K range is above the median for Data Scientist roles in our dataset (median: $167K across 150 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 8,943 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Vikta Energy, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,406 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. Disclosed range: $160K to $220K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Vikta Energy AI Hiring
Vikta Energy has 1 open AI role right now. They're hiring across Data Scientist. Based in US. Compensation range: $220K - $220K.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below the national median.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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