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About This Role
Description
### POSITION DESCRIPTION:
We are seeking a motivated Junior Data Scientist (Temporary) to join our Grid \& Energy Analytics team a short‑term assignment. This role is ideal for graduate students currently studying data science, computer science, engineering, or applied mathematics, who are excited about applying AI techniques to real‑world energy systems.
During this internship, the candidate will work closely with senior data scientists and the wholesale market applications team to build and fine‑tune energy AI agents, contribute to forecasting and optimization models, deploy AI models into production‑grade systems, and gain hands‑on exposure to state‑of‑the‑art research in energy optimization and agentic AI.
### RESPONSIBILITIES
- Assist in building, fine‑tuning, and evaluating AI agents designed for energy forecasting, decision‑making, and optimization tasks.
- Support development of time‑series models for forecasting energy demand, solar generation, wholesale market prices, and ancillary services.
- Help explore and test new ML algorithms or MLOps architectures relevant to energy system challenges.
Data Engineering \& Model Evaluation
- Clean, preprocess, and analyze large‑scale energy datasets using Python‑based data science tools.
- Help evaluate model performance, perform error analysis, and implement incremental improvements.
Production Model Deployment
- Work with senior engineers to package, test, and deploy AI/ML models in real operational environments.
- Assist in monitoring model performance and troubleshooting basic issues in production workflows.
Research \& Experimentation
- Stay up\-to\-date with emerging techniques in ML, forecasting, and AI agent design.
- Contribute to internal experiments investigating novel approaches for energy optimization and real‑time decision‑making.
Collaboration \& Communication
- Collaborate with the data science team, wholesale market applications team, and software engineering teams.
- Present findings, experiments, and results in informal team updates.
### REQUIRED QUALIFICATIONS
- Education \- Currently enrolled in or recently completed an undergraduate or graduate program in data science, computer science, engineering, applied mathematics, statistics, or a related field.
Technical Skills* Basic proficiency in Python and common data science libraries (pandas, numpy, scikit‑learn, matplotlib).
- Familiarity with machine learning concepts (regression, classification, time‑series basics).
- Coursework or project experience in ML, AI, optimization, or statistics.
- Interest in learning about energy markets, renewable energy systems, and energy optimization.
Soft Skills* Strong curiosity and willingness to learn.
- Ability to work collaboratively with cross‑functional technical teams.
- Good communication skills and ability to present findings clearly.
- Self‑driven, organized, and comfortable with fast‑paced environments.
### PREFERRED QUALIFICATIONS
- Familiarity with energy systems, renewable energy, or electricity markets.
- Exposure to deep learning tools (PyTorch, TensorFlow) or time‑series packages (nixtla).
- Experience with AI agents, reinforcement learning, or RAG pipelines.
- Previous internship or project experience with ML or MLOps tools.
What you will learn* How real‑world energy AI agents are built and deployed.
- How production‑grade forecasting models are tested, scaled, and monitored.
- How the electricity grid, energy markets, and renewable assets interact.
- Best practices in software engineering, MLOps, and data science workflows.
- Exposure to cutting‑edge AI and optimization research used inside a global renewable energy company.
### DURATION \& STRUCTURE
- Flexible: 1 \- 6 months
- Timing: May 19, 2026 – August 19, 2026
- Full‑time
Hanwha Q CELLS Technologies, Inc. a subsidiary of Hanwha Q CELLS, one of the world´s largest and most recognized photovoltaic manufacturers for its high\-performance, high\-quality solar cells and modules. It is headquartered in Seoul, South Korea (Global Executive HQ) Talheim, Germany (Technology \& Innovation HQ) and Santa Clara, CA, USA (HW and SW Product Development HQ). Through its growing global business network spanning Europe, North America, Asia, South America, Africa, and the Middle East, the company provides excellent services and long\-term partnerships to its customers in the utility, commercial, government, and residential markets. Hanwha Q CELLS is a flagship company of Hanwha Group, a FORTUNE Global 500 firm and a Top 7 business enterprise in South Korea.
PHYSICAL, MENTAL \& ENVIRONMENTAL DEMANDS:
To comply with the Rehabilitation Act of 1973 the essential physical, mental and environmental requirements for this job are listed below. These are requirements *normally expected* to perform *regular* job duties. Incumbent must be able to successfully perform all of the functions of the job with or without reasonable accommodation. Mobility
Standing
20% of time
Sitting
70% of time
Walking
10% of time
Strength
Pulling
up to 10 Pounds
Pushing
up to 10 Pounds
Carrying
up to 10 Pounds
Lifting
up to 10 Pounds
Dexterity (F \= Frequently, O \= Occasionally, N \= Never)
Typing
F
Handling
F
Reaching
F Agility (F \= Frequently, O \= Occasionally, N \= Never)
Turning
F
Twisting
F
Bending
O
Crouching
O
Balancing
N
Climbing
N
Crawling
N
Kneeling
N
The salary range is required by the California Pay Transparency Act and may differ depending on the location of those candidates hired nationwide. Actual compensation is influenced by a wide array of factors including but not limited to, skill set, education, licenses and certifications, essential job duties and requirements, and the necessary experience relative to the job’s minimum qualifications.
- This target salary range is for CA positions only and should not be interpreted as an offer of compensation.
You may view your privacy rights by reviewing Qcells' Privacy Policy or by contacting our HR team for a copy.
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 26,159 AI roles we're tracking, Data Scientist positions make up 2% of the market. At Qcells, 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 $204,700 based on 441 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $76,880.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Qcells AI Hiring
Qcells has 3 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Teaneck, NJ, US, Santa Clara, CA, US.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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