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About This Role
What You Can Expect Data is at the heart of ENGIE’s mission to provide sustainable energy clients with superior insights that enable strategic, actionable trading and dispatch decisions. Every day, the Global Market Analysis team forecasts hundreds of millions of electric grid data points using a combination of physical modeling, statistics, and artificial intelligence.
As a Senior Data Scientist, you will play a critical role in advancing ENGIE’s forecasting platform across multiple markets and time horizons. This position will focus on short-term power price forecasting and will work closely with battery trading and optimization teams to improve forecast accuracy, drive profitability, and support operational decision-making. The role requires strong analytical thinking, intellectual curiosity, and the ability to bridge physical system understanding with advanced computational modeling
- Analyze, develop, test, and improve forecasting models used to predict electricity prices and support trading decisions
- Own and enhance short-term forecasting workflows that generate updated forecasts throughout the day
- Partner closely with trading and optimization teams to evaluate forecast performance and determine the impact of forecast quality on trading profitability
- Expand forecasting capabilities across multiple U.S. power markets, including ERCOT, PJM, SPP, and other emerging opportunities
- Design and execute experiments to identify sources of forecast error and improve probabilistic forecasting performance
- Leverage statistics, machine learning, AI, and physical system models to build robust forecasting solutions
- Develop production-level Python code that supports large-scale model training, backtesting, and deployment in cloud-based environments
- Contribute to internal machine learning infrastructure, forecasting pipelines, and AI-enabled development tools
- Collaborate with a multidisciplinary team of experts in power systems, weather analytics, artificial intelligence, and market modeling
What You’ll Bring
- Bachelor's degree in Data Science, Computer Science, Engineering, Mathematics, Physics, Statistics, or another quantitative STEM discipline.
- Minimum 5 years of experience working as a Data Scientist, Machine Learning Engineer, Quantitative Analyst, or similar role.
- Minimum 10 years of relevant work experience
- Advanced proficiency in Python and experience developing production-quality code.
- Strong knowledge of machine learning, statistical analysis, uncertainty quantification, and predictive modeling.
- Strong understanding of linear algebra, probability, and experimentation methodologies.
- Experience working with large datasets and building scalable analytical workflows.
- Ability to communicate technical concepts, experimental results, and recommendations clearly to technical and business stakeholders.
- Demonstrated critical thinking, problem-solving, and analytical capabilities.
- Strong attention to detail and commitment to delivering high-quality solutions.
- Team-oriented mindset with a collaborative approach to problem solving.
Additional Details
- This role is eligible for our hybrid work policy; a minimum of 3 days working in the office per week is required
- Must be available to travel domestically or internationally up to 10% of the time
- Must be willing and able to comply with all ENGIE ethics and safety policies;
Compensation Salary Range: $135,000 – $206,885 USD annually
This represents the average expected pay range for a qualified candidate.
ENGIE complies with all federal, state, and local minimum wage laws. Actual salary offered may vary depending on geography, experience, education, internal pay alignment, or other bona fide factors.
In addition to base pay, this position is eligible for a competitive bonus / incentive plan.
Your Talent Acquisition Partner can share more specific information regarding the benefits or the salary for the position based on the work location.
At ENGIE, we take your well-being seriously. Our comprehensive benefits package includes options for medical, dental, vision, life insurance, employer-paid short-term and long-term disability insurance, ESPP, generous paid time off including wellness days, holidays and leave programs. We also help you plan for retirement by offering a 401(k) Retirement Savings Plan with a company match. But that's not all – we're dedicated to the health and happiness of your entire family, offering supplemental benefits for full time employees that enhance emotional and physical well-being through all stages of life from family forming to caregiver benefits. Explore our benefits package to see how we can support you. Learn more.
Why ENGIE? ENGIE North America isn’t just participating in the Zero-Carbon Transition, we’re leading it! Join us as we develop energy that is renewable, efficient, and accessible to everyone.
At ENGIE, every talent has a role to play in the adventure of the century. Make a difference and enjoy a fulfilling professional experience. Take on exciting challenges and build a career path that reflects who you are.
Join us and be part of the adventure!
ENGIE is proud to be an equal opportunity workplace, and we are firmly committed to creating an inclusive workplace for all employees. We are committed to providing employees with a work environment free of discrimination and harassment. All qualified applicants will receive consideration for employment without regard to race, color, sex, sexual orientation, gender identity, religion, national origin, disability, veteran status, or other legally protected status.
If you need assistance with this application or a reasonable accommodation due to a disability, you may contact us at [email protected]. This email address is reserved for individuals with disabilities in need of assistance and is not a means of inquiry regarding positions or application status.
We value internal mobility and will offer sponsorship for this role to internal candidates only.
The safety of our employees is our number one priority. All employees at ENGIE have both a duty and the authority to STOP WORK if unsafe acts are observed. Company Name: Supply and Energy Management Minimum Base Salary: 135,000.00 USD Maximum Base Salary: 206,885.00 USD Pay Basis: Annual
Why this matters to us
Our organisation is an equal opportunity employer and is committed to fostering a diverse and inclusive workplace. We offer reasonable accommodations upon request for individuals with disabilities.
Salary Context
This $135K-$206K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At ENGIE, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($170K) sits 19% below the category median. Disclosed range: $135K to $206K.
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.
ENGIE AI Hiring
ENGIE has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Houston, TX, US. Compensation range: $206K - $206K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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.
Frequently Asked Questions
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