Interested in this Data Scientist role at Power Factors?
Apply Now →Skills & Technologies
About This Role
ABOUT POWER FACTORS
Power Factors is a leading software and solutions provider supporting the next generation of clean energy through Unity, one of the most comprehensive and widely deployed Renewable Energy Management Suites (REMS) in the market. The company manages more than 300 GW of wind, solar, and energy storage assets globally, serving over 600 customers and 18,000 sites.
Power Factors' Unity REMS suite spans the full energy value chain — from supervision and control through advanced analytics and market analysis. Through open, data\-driven applications, the platform enables renewable energy organizations to automate critical processes, integrate complex operational data, and make high\-confidence decisions to optimize asset performance and lifecycle value.
Drawing on deep domain expertise, Power Factors deploys advanced analytics and AI at scale to enable asset owners and operators to improve reliability, availability, and long\-term value as the global energy system transitions to clean energy.
Power Factors fights climate change with code.
*For more information: powerfactors.com*
ABOUT THE POSITION
As a Senior Data Scientist – BESS, you will lead the design and development of scientifically rigorous analytics solutions leveraging Power Factors' extensive operational data, covering more than 300 GW of renewable energy assets — including one of the world's largest battery energy storage datasets.
This role is centered on deep BESS domain expertise, advanced statistical and machine learning methods, and large\-scale operational data. You will work hands\-on in Python and modern AI frameworks to develop production\-grade analytical models that address complex problems in battery performance, state\-of\-health estimation, degradation forecasting, dispatch optimization, and asset lifecycle management.
You will collaborate closely with software development teams, product management, and technically sophisticated customers to translate real\-world operational challenges into robust analytical solutions integrated into Power Factors' core platform. Your work will directly inform product capabilities used by leading BESS asset owners and operators globally.
This position offers the opportunity to apply advanced analytics at unprecedented scale to materially improve the performance and economics of battery energy storage assets worldwide.
ABOUT YOU
You are a senior\-level data scientist and recognized BESS subject matter expert with substantial experience working with utility\-scale battery energy storage systems. You possess deep understanding of battery electrochemistry, BMS data, system\-level operations, performance drivers, degradation mechanisms, and grid service delivery — and you have applied advanced analytics to these challenges in real operational environments.
You are highly proficient in Python, statistical modeling, machine learning, and AI, with a track record of interpretable, physics\-informed, and operationally robust approaches. You are comfortable working with real\-world high\-frequency industrial time series and understand how analytical assumptions translate into operational and commercial decisions.
You work effectively with software engineers and are comfortable contributing to production\-quality code, reviewing model implementations, and discussing tradeoffs related to scalability, latency, and maintainability.
You are motivated by solving hard, real\-world problems in energy storage using advanced analytics and by seeing your work deployed at scale across large, diverse BESS fleets.
REQUIRED QUALIFICATIONS
Industry Experience and BESS Expertise
- 5\+ years of experience in the energy industry, with a strong emphasis on utility\-scale battery energy storage systems
- Deep, hands\-on understanding of BESS operation, performance, state\-of\-charge and state\-of\-health estimation, thermal management, degradation mechanisms, and lifecycle management
- Demonstrated experience working with large\-scale operational BESS datasets, including BMS telemetry, SCADA, event logs, cycle data, and high\-frequency time series
- Proven experience developing analytical solutions used by asset owners, operators, integrators, or OEM\-adjacent organizations to support operational and strategic decision\-making
Technical Skills
- Proficiency in Python and modern data science libraries (pandas, NumPy, scikit\-learn, TensorFlow / PyTorch, etc.)
- Strong grounding in statistics, machine learning, and applied AI, with demonstrated real\-world deployment experience
- Experience with time\-series analysis, signal processing, anomaly detection, forecasting, and predictive modeling in industrial or electrochemical contexts
- Experience with version control (Git) and collaborative software development practices
Collaboration and Communication
- Ability to clearly communicate complex analytical concepts, assumptions, and results to both technical and non\-technical stakeholders
- Experience working closely with software engineering teams in production environments (APIs, services, deployment pipelines)
- Proven ability to translate operational challenges and customer needs into analytically sound and implementable solutions
Approach and Mindset
- High degree of autonomy and ownership in complex, technically ambiguous problem spaces
- Strong scientific rigor, intellectual curiosity, and attention to detail
- Comfort working on long\-horizon, technically demanding problems where analytical quality and robustness are critical
QUALIFICATIONS CONSIDERED AS ASSETS
- Ph.D. or master's degree in electrochemical engineering, physics, applied mathematics, statistics, computer science, or a related quantitative field
- Specialized experience in BESS\-specific analytics such as:
- Electrochemical model\-based or data\-driven SoH and RUL estimation
- Degradation and calendar/cycle life modeling
- Dispatch optimization and revenue stack modeling
- Augmentation strategy and capacity fade forecasting
- Thermal runaway risk modeling or fault detection
- Experience with real\-time or near\-real\-time analytics pipelines
- Hands\-on experience with data engineering concepts, cloud platforms (AWS, Azure, or GCP), and scalable data pipelines
- Knowledge of power markets, capacity markets, ancillary services, PPAs, or grid interconnection as they relate to BESS assets
- Experience with MLOps practices and large\-scale model deployment
- Scientific publications, patents, or technical conference presentations in battery analytics, applied machine learning, or related fields
RESPONSIBILITIES
Advanced Analytics and Product Development
- Identify, evaluate, and prioritize high\-impact advanced analytics opportunities focused on BESS performance, health, and revenue optimization
- Lead the development of advanced data science solutions from concept through production deployment
- Leverage Power Factors' large\-scale operational BESS database to build analytically rigorous, scalable models used across global storage fleets
- Collaborate with product and engineering teams to ensure analytical solutions are robust, interpretable, and operationally actionable
Domain Expertise and Customer Engagement
- Apply deep BESS domain knowledge to define analytically sound problem formulations, feature engineering strategies, and model validation approaches
- Engage directly with technically sophisticated customers to explain analytical methods, assumptions, and results with clarity and precision
- Serve as an internal BESS subject matter expert, supporting product, sales, and customer success teams with domain guidance
Research and Innovation
- Stay current with advances in battery science, electrochemical modeling, grid storage economics, and ML for energy systems
- Propose and lead experiments to improve model quality, expand analytical coverage, and deliver new product capabilities
- Contribute to the external scientific and technical community through publications, conference presentations, or open\-source contributions where appropriate
LIFE @ POWER FACTORS
We are an agile software development company – big enough to make an impact, but small enough to move quickly and execute in a growing industry. We are a team of bold and ingenious talents driven by results. We are passionate about making the world a better place. We seek fierce and humble people to help us achieve our ambitious plan.
WHY JOIN US
By joining the Power Factors team, you’ll be part of a dynamic group of innovative and driven individuals dedicated to making a positive impact. Every day, your work will directly contribute to advancing clean energy solutions and supporting global sustainability initiatives. Our culture runs deep and shows up in how we work together \- committed, conscientious and collaborative. With many opportunities for professional growth, Power Factors is here to support your development as we lead the charge in transforming the energy industry.
WE ARE AN EQUAL OPPORTUNITY EMPLOYER
Power Factors is an Equal Opportunity Employer committed to engaging a diverse workforce and sustaining an inclusive culture. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.
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 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Power Factors, 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 $192,890 based on 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Power Factors AI Hiring
Power Factors has 1 open AI role right now. They're hiring across Data Scientist. Based in Austin, TX, US.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.