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About This Role
Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers.
Scope of the Role:
We are looking for a highly skilled Research Data Scientist – GenAI/LLM to join our AI/LLM Delivery Unit and work on research-driven AI/ML initiatives involving Generative AI, Large Language Models (LLMs), NLP, multimodal AI, model evaluation, and AI data.
The role combines strong research and analytical capabilities with hands-on AI/ML expertise, requiring the candidate to design experiments, develop evaluation methodologies, analyze complex datasets, build research prototypes, and translate research findings into practical AI/ML solutions.
The ideal candidate will have a strong research orientation, excellent statistical and analytical skills, and the ability to work collaboratively with researchers, data scientists, AI/ML engineers, domain experts, and client-facing teams.
What You'll Own:
AI/ML & Generative AI Research:
- Conduct independent and collaborative research in Generative AI, LLMs, NLP, multimodal AI, machine learning, model evaluation, and AI data.
- Formulate research questions and translate complex AI/ML problems into structured research methodologies and experiments.
- Design, execute, and analyze experiments to evaluate and improve AI/ML models and solutions.
- Build analytical models, prototypes, and research pipelines using Python and relevant ML frameworks.
- Stay current with emerging research, methodologies, papers, and developments in GenAI, LLMs, NLP, multimodal models, and AI evaluation.
LLM & Model Evaluation:
- Develop and implement LLM evaluation frameworks, benchmarks, datasets, and evaluation criteria.
- Evaluate models for accuracy, robustness, bias, hallucination, reasoning, relevance, response quality, and other performance dimensions.
- Conduct model benchmarking, error analysis, comparative analysis, and performance evaluation.
- Work on areas such as RAG, SFT, RLHF/DPO, prompt engineering, fine-tuning, embeddings, and LLM optimization, as applicable.
- Identify model and data gaps and recommend improvements to enhance model performance and reliability.
Data Science & Statistical Research:
- Collect, clean, analyze, and interpret large and complex structured and unstructured datasets.
- Perform EDA, statistical analysis, hypothesis testing, significance testing, correlation analysis, sampling, and error analysis.
- Develop data-driven insights and identify patterns, trends, and relationships relevant to AI/ML research.
- Apply appropriate statistical and quantitative methodologies to validate research findings
AI Data & Dataset Development:
- Develop and evaluate datasets, sampling methodologies, taxonomies, annotation frameworks, data quality frameworks, and evaluation criteria for AI/ML models.
- Analyze data quality and identify issues affecting model performance.
- Collaborate with annotation, data engineering, and AI/ML teams to improve AI training and evaluation data.
- Translate data and research findings into actionable recommendations for improving AI system
Research & Innovation:
- Contribute to research papers, technical reports, whitepapers, patents, benchmarks, internal publications, and other research outputs, where applicable.
- Identify opportunities to apply emerging research and technologies to real-world AI and data challenges.
- Explore new methodologies, models, datasets, and evaluation approaches to improve AI capabilities.
- Contribute to capability building and innovation within the AI/LLM practice
Collaboration & Stakeholder Engagement:
- Work closely with researchers, data scientists, AI/ML engineers, data/annotation teams, domain experts, and delivery teams.
- Present research findings, analytical insights, and technical recommendations to senior technical stakeholders.
- Translate complex research and technical concepts into clear, actionable recommendations.
- Where required, participate in client-facing technical discussions and presentations and help translate business requirements into AI/ML solutions.
You'll Thrive in This Role If You Have:
- Master's or PhD in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Statistics, Mathematics, Computational Science, or a related discipline.
- Bachelor's/Master's degree from IITs, NITs, or other premier engineering/research institutions is strongly preferred.
- 4–7 years of hands-on research experience in AI/ML, Data Science, NLP, Generative AI, LLMs, or related areas.
- Strong demonstrated research experience with the ability to independently formulate research questions, design experiments, analyze results, and communicate findings.
- Demonstrated research track record through research publications, patents, conference presentations, open-source contributions, or significant AI/ML research projects.
- Candidates with publications in reputed conferences/journals and a strong academic/research profile will be preferred
- Strong proficiency in Python and SQL.
- Strong hands-on experience with NumPy, Pandas, Scikit-learn, and preferably PyTorch/TensorFlow.
- Strong understanding of: + Machine learning algorithms + Statistics and experimentation + Data analysis and feature engineering + Model evaluation and performance metrics + Hypothesis testing and statistical inference
- Hands-on exposure to LLMs, NLP, Generative AI, and multimodal AI.
- Experience with one or more of RAG, LLM evaluation, prompt engineering, fine-tuning, SFT, RLHF/DPO, embeddings, or model benchmarking.
- Experience working with large-scale structured and unstructured datasets.
- Familiarity with Git and cloud platforms such as AWS, Azure, or GCP is desirable.
The expected salary range for this position is $160,000 - $185,000 p/year, based on experience, skills, and qualifications.
Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission's guide at https://consumer.ftc.gov/articles/job-scams.
If you believe you've been targeted by a recruitment scam, please report it to Innodata at [email protected] and consider reporting it to the FTC at ReportFraud.ftc.gov*.*
Salary Context
This $160K-$185K range is above the median for Data Scientist roles in our dataset (median: $167K across 172 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 9,653 AI roles we're tracking, Data Scientist positions make up 5% of the market. At Innodata, 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($172K) sits 9% below the category median. Disclosed range: $160K to $185K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Innodata AI Hiring
Innodata has 1 open AI role right now. They're hiring across Data Scientist. Based in New York, NY, US. Compensation range: $185K - $185K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% above 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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