Role Description As an Applied Data Scientist, Health AI Evaluation & Datasets, you own the design, measurement quality, and clinical validity of datasets used to train, fine-tune, and evaluate health-domain models. You bring clinical or biomedical fluency and data science rigor. - Translate customer goals into dataset specifications, taxonomies, rubrics, sampling plans, and acceptance criteria. - Make multimodal health AI a core focus, designing training and evaluation datasets across various clinical artifacts. - Design evaluations for retrieval-augmented and source-grounded health AI systems. - Define sampling strategies, label schemas, inter-annotator agreement targets, and quality thresholds. - Build statistical and ML checks to ensure healthcare datasets are trustworthy. - Partner with Applied Research Scientists and AI/ML Research Engineers to instrument datasets into evaluation and post-training pipelines. - Evaluate health AI behavior beyond surface accuracy, considering clinical workflow fit. - Own data quality from source intake through delivery, ensuring compliance and proper handling of sensitive information. - Stay current on the health AI landscape, including regulatory developments and emerging evaluation methodologies. - Support customer discovery and proposal work by scoping dataset programs and explaining methodology choices. - Contribute to Innodata internal IP, including reusable health-domain taxonomies and evaluation rubrics. Qualifications - 5+ years of data science experience, including at least 2+ years in healthcare or related fields. - Working knowledge of healthcare data and standards, including EHR structure and clinical documentation conventions. - Hands-on experience designing ML datasets, including writing annotation guidelines and setting quality thresholds. - Familiarity with LLM-based health AI workflows and the limitations of automated evaluation in clinical contexts. - Strong Python and SQL skills; familiarity with tools like pandas and scikit-learn. - Statistical literacy across sampling design and bias analysis. - Solid grasp of healthcare privacy, compliance, and governance. - Ability to work credibly with clinicians, biomedical SMEs, and technical teams. - A bias toward clinical realism in dataset construction. - Degree in a relevant field such as biostatistics, health informatics, or computer science with a health focus. - Clinical credentials are not required, but candidates with relevant backgrounds are encouraged. Requirements - The expected salary range for this position is $150,000 – $175,000 USD per year, based on experience, skills, and qualifications. Company Description Innodata (Nasdaq: INOD) is a global data engineering company focused on enabling the responsible advancement of artificial intelligence.

Role Description As an Applied Data Scientist, Financial AI Evaluation & Datasets, you own the design, measurement quality, and domain validity of the datasets used to train, fine-tune, evaluate, and monitor financial-domain LLMs, vision-language models, multimodal document models, and AI agents. - Translate customer goals into concrete dataset specifications, taxonomies, rubrics, and acceptance criteria. - Design training and evaluation datasets across the financial AI surface: financial QA, filings and earnings analysis, credit and underwriting, fraud/AML investigation, and compliance. - Foreground unstructured and multimodal financial data in dataset design: PDFs, scanned statements, tables, charts, and call transcripts. - Design datasets and evaluations for retrieval-augmented and source-grounded systems. - Evaluate agentic and workflow-integrated financial AI systems. - Develop evaluation methodology that goes beyond surface accuracy. - Define sampling strategies, label schemas, and adjudication workflows with Language Data Scientists and finance SMEs. - Build the statistical and ML tooling that makes large financial datasets trustworthy. - Build evaluation and dataset-quality evidence to support financial-services model risk management. - Partner with the AI/ML Research Engineer to instrument datasets into training, evaluation, and monitoring pipelines. - Own data quality end-to-end, from intake through delivery. - Reason about financial workflow context. - Support the Technical Solutions Architect during customer discovery and proposals. - Stay current on the financial AI landscape. - Contribute to Innodata internal IP. Qualifications - 5+ years of data science experience, with at least 2+ years in financial services, fintech, banking, or a comparable regulated data environment. - Real working knowledge of financial data and workflows. - Hands-on experience with unstructured and multimodal financial data. - Familiarity with financial standards or protocols such as XBRL, ISO 20022, or GAAP/IFRS reporting concepts is strongly preferred. - Hands-on experience designing datasets for ML. - Familiarity with LLM-based and multimodal financial AI workflows. - Strong Python and SQL; comfort with pandas, scikit-learn, or equivalent. - Statistical literacy. - Solid grasp of financial services privacy, compliance, and governance. - Excellent collaboration skills. - A bias toward financial workflow realism. - Degree in a relevant field — statistics, data science, economics, finance, or a related quantitative field. - Experience designing evaluations for LLMs, VLMs, or multimodal models in financial reasoning, filings analysis, or fraud/AML contexts. - Experience with document AI, OCR/post-OCR quality, or table and chart extraction for complex financial documents. - Familiarity with agentic evaluation, AI observability, experiment tracking, or tools such as Weights & Biases or LangFuse. - Familiarity with model risk management frameworks, validation documentation, fairness/bias auditing, or consumer protection analysis. - Experience with multilingual or cross-border financial data. Requirements - The expected salary range for this position is $150,000 – $175,000 USD per year, based on experience, skills, and qualifications. Company Description Innodata (Nasdaq: INOD) is a global data engineering company focused on enabling the responsible advancement of artificial intelligence.

• Build and own the company's data function from the ground up • Audit and improve existing data infrastructure, tooling, and processes • Lead a lean data team while remaining hands-on with analysis • Leverage AI to automate workflows and increase team productivity • Analyze large datasets to answer strategic business questions • Deliver investment-grade analyses, dashboards, and executive briefings • Establish data governance, reporting standards, and analytical best practices • Partner with Product, Finance, Operations, and Leadership to drive business decisions • Continuously improve the company's AI-enabled data capabilities

• Build and own the company's data function from the ground up • Audit and improve existing data infrastructure, tooling, and processes • Lead a lean data team while remaining hands-on with analysis • Leverage AI to automate workflows and increase team productivity • Analyze large datasets to answer strategic business questions • Deliver investment-grade analyses, dashboards, and executive briefings • Establish data governance, reporting standards, and analytical best practices • Partner with Product, Finance, Operations, and Leadership to drive business decisions • Continuously improve the company's AI-enabled data capabilities