• Partner with product teams to clarify business problems, define success metrics, and translate ambiguous questions into structured analytical plans. • Design, run, and analyze experiments (A/B tests, pre/post, quasi-experimental reads) to measure impact, quantify trade-offs, and guide iteration. • Perform deep-dive product analysis (funnels, cohorts, segmentation, sizing) to uncover drivers of performance and identify opportunities for growth and customer experience improvements. • Build reliable datasets and dashboards using SQL and modern BI tools, enabling self-serve product performance monitoring across platforms (web, app, partner). • Apply solid statistical thinking (probability, sampling, inference, regression) to ensure reads are robust, distinguish signal from noise, and clearly communicate caveats. • Use Python/R (or similar) to prototype models and advanced analyses when needed (e.g., regression, clustering, simple prediction) and to automate recurring analytics workflows. • Tell clear, compelling stories that move stakeholders from insight to action—framing context, methods, results, and recommendations for both technical and non-technical audiences. • Champion data quality and reproducibility by following best practices for data validation, query performance, version control, and documentation. • Collaborate and upskill others by seeking peer review, sharing best practices, and providing light mentorship to more junior analysts or data scientists.

Role Description They are seeking a Data Scientist to join the team. Do you love working in machine learning pipelines with big data from day one? Do you have hands-on experience in state-of-the-art NLP (e.g., transformers, BERT, few-shot learning, fine-tuning) and information retrieval (e.g., vector-based semantic search, reranking, hybrid search)? Do you want to continue to work at the frontier of advanced ML and NLP, at a mission-driven startup where they love what they do? If this sounds like you, this might be a dream job! What You’ll Own - Recommender Systems: Ensuring the product makes high-quality recommendations to both job seekers (as a “GPS” for their careers) and employers (as a “talent radar”). - Taxonomies: Using data and AI to model and make sense of the evolving landscape of skills, occupations, and careers in a changing labor market. - Agentic AI Solutions: Prototyping and evaluating new AI-powered functions for job seekers, employers, and case workers. - Data Science Strategy: Serving as an advisor and expert on strategy and roadmap decisions, evaluating major developments in the field, and ensuring the use of state-of-the-art techniques. - Mentorship/Leadership: Acting as a dedicated mentor and tech lead, assisting with the quality, rigor, and acceleration of projects and tasks. Qualifications - 5+ years of combined experience in industry and/or graduate-level research. - Proficiency training and evaluating ML and NLP models using both structured and unstructured data with reproducible results. - Expert-level programming and software engineering skills and a track record of delivering re-usable and well-documented code. - Practical knowledge of using LLMs to accelerate model development, for example through synthesizing training data or automating evaluation. - Proven ability to own a problem space from 0 to 1, translating vague business problems into production models. - Comfortable navigating ambiguity and working with competing priorities. - Strong communication skills to present models and actionable insights to stakeholders with varying levels of technical expertise. Bonus Points - Specific examples of recommendation systems, taxonomies, or AI agents that you have built. - Experience with data engineering, analytics, dashboards, or BI tools. - Experience with A/B testing or causal analysis. - An economics or social sciences background, especially any experience analyzing or modeling labor market data. - Recognized expertise through publications, conference talks, open-source software releases, or other public artifacts related to state-of-the-art tools, models, and techniques. Our Tech Stack - Languages: SQL, Python - Data orchestration: Airflow - Data storage and warehousing: S3, Glue, Redshift, PostgreSQL, MongoDB - Machine learning and experimentation: AWS SageMaker - Visualization and reporting: Looker, QuickSight - Infrastructure: AWS ecosystem Your Education Your alma mater isn’t their focus. Your grit, hunger, and drive are. If you learn continuously, tackle challenges head-on, and know your strengths and gaps intimately—you’re their person. Location United States or Canada (Remote, ET Preferred) Travel Expectations You may be expected to travel up to once per quarter. Compensation The base salary range for this role is $115,000-$140,000. This range reflects the varying levels of expertise and responsibilities that will be determined through the interview process, based on applied experience and other criteria established by the hiring committee. Hiring Journey The hiring process is designed to help you assess whether this role and the culture are the right fit based on your unique skills, mindset, and experiences. They move fast and work with intensity, so they want you to get a real sense of that from the start. - Online Application - Initial Screen with Director of People & Culture - Interview with Hiring Manager - Performance Challenge - Panel Interview - Final Decision Generally, this entire process takes around 4 weeks, although the timing can vary due to specific candidate circumstances. Company Snapshot - Team: 30-50 across US and Canada (hubs in NYC and Toronto) - Customers: Workforce development agencies and intermediaries, government agencies, employers - Industry: SaaS/AI technology - Funding: Bootstrapped 0-1, then raised funding led by JP Morgan - Structure: Growth, Customer Success, Product, Engineering, Data, People & Culture, Finance & Operations Our Core Principles - Be Curious - Drive to Outcomes - Raise the Bar - Speed Matters - Own It - We Over Me Use of AI in Hiring They use artificial intelligence (AI) tools to make the hiring process more efficient, consistent, and equitable—never to replace human judgment. AI is used in the following ways: - Screening support: AI may help compare applications against the skills and experience required for a specific role. These skills are defined by the hiring team for each position. A human reviews each application, with the AI assessment as just one input. - Interview support: In some interviews, they may use an AI notetaker to summarize the discussion so interviewers can focus on being present in the conversation. - Insights, not decisions: AI provides data points to support the team’s evaluation but does not make or recommend final hiring decisions. Every hiring decision is made by people. They will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, perform essential job functions, and receive other benefits and privileges of employment. Please contact them to request an accommodation. They are proud to be an equal opportunity workplace. They celebrate diversity and are committed to creating an inclusive environment for all employees. They do not discriminate on the basis of race, religion, color, gender identity, sexual orientation, age, disability, veteran status, or other applicable legally protected characteristics. They encourage people of different backgrounds, experiences, abilities, and perspectives to apply.

Role Description This position will support computational oncology and cancer informatics research initiatives focused on transforming complex clinical data into structured, actionable datasets for research, quality improvement, clinical trial identification, and care delivery optimization. The role will emphasize applied machine learning, natural language processing, and large language model-driven workflows using real-world clinical data, including electronic health record data, pathology reports, radiology reports, clinical notes, genomics, treatment data, and other institutional data sources. The Data Scientist will work semi-independently in close collaboration with clinical investigators, informatics teams, biostatisticians, and other data science stakeholders to design, build, evaluate, and refine computational pipelines. The ideal candidate will have practical prior experience developing data science workflows in Python and using modern machine learning or LLM-based tools in real projects. Job Responsibility - Develop, test, and maintain Python-based data pipelines for clinical research, quality improvement, and computational oncology projects. - Support cancer informatics projects involving natural language processing, machine learning, large language models, and structured extraction from unstructured clinical data. - Build workflows for processing clinical notes, pathology reports, radiology reports, treatment records, genomics reports, and other real-world healthcare data sources. - Implement and evaluate LLM-assisted workflows, including prompt engineering, structured output generation, model benchmarking, validation pipelines, and error analysis. - Assist with the development of retrieval-augmented generation workflows, vector search, embedding-based retrieval, and related approaches where appropriate. - Work with clinical subject matter experts to translate oncology-focused research questions into executable data science tasks. - Perform data cleaning, data wrangling, exploratory analysis, feature engineering, model development, and model performance evaluation. - Generate reproducible analyses, reports, dashboards, tables, and visualizations to communicate findings to clinical and operational stakeholders. - Maintain clear documentation of code, analytic decisions, model assumptions, validation methods, and project outputs. - Participate in model validation efforts, including comparison of computational outputs against clinician-reviewed reference standards. - Contribute to manuscript, abstract, grant, and presentation development through data analysis, figure generation, and methods documentation. - Work independently on assigned analytic tasks while communicating progress, limitations, and blockers clearly to project leadership. Qualifications - Bachelor’s Degree in Computer Science, Informatics, Statistics, Engineering, Data Science, or related field, required. Master’s Degree, preferred. - Minimum of two (2) years of post-graduate training or experience involving quantitative data analysis, required and working with clinical data, data science, and machine learning, preferred. - Working familiarity with basic medical and health information technology concepts, including standardized terminologies and ontologies and electronic health records, as well as Data Warehousing and Business Intelligence tools, required. - Expertise in working with SQL relational databases and statistical or general programming languages (e.g., Python, R), required. - Deep understanding of statistical and predictive modeling concepts, machine-learning approaches, clustering and classification techniques, and recommendation and optimization algorithms. Requirements - Demonstrated prior experience building or implementing applied data science, machine learning, NLP, or LLM-based workflows. Completion of a short AI certificate, bootcamp, or introductory course alone is not sufficient for this role. - Strong practical experience with Python for data science, including pandas, NumPy, scikit-learn, Jupyter notebooks, and reproducible analytic workflows. - Prior experience applying machine learning, natural language processing, or large language models to real-world data problems. - Experience using off-the-shelf LLMs through APIs or enterprise platforms, including structured prompting, output parsing, evaluation, and workflow integration. - Experience with retrieval-augmented generation, vector databases, embeddings, semantic search, or document retrieval pipelines. - Experience working with clinical, biomedical, or electronic health record data. - Familiarity with oncology data, cancer registries, pathology reports, radiology reports, genomics reports, or clinical trial data. - Experience working in secure data environments, enterprise data warehouses, Databricks, Spark, SQL databases, or cloud-based analytic platforms. - Ability to write clean, maintainable, well-documented code and use version control such as Git. - Demonstrated ability to work semi-independently, manage multiple analytic tasks, and communicate technical concepts to non-technical clinical collaborators. - Prior experience contributing to academic research, abstracts, manuscripts, grant-funded projects, or healthcare quality improvement initiatives. - Understanding of model evaluation concepts including accuracy, precision, recall, F1 score, calibration, error analysis, and external validation. - Experience with prompt engineering alone is not sufficient; candidates should have substantive prior experience in data science, machine learning, computational research, or applied analytics. Benefits - The salary range and/or hourly rate listed is a good faith determination of potential base compensation that may be offered to a successful applicant for this position at the time of this job advertisement and may be modified in the future. - When determining a team member's base salary and/or rate, several factors may be considered as applicable (e.g., location, specialty, service line, years of relevant experience, education, credentials, negotiated contracts, budget and internal equity).

• Time Series & ML Engineering: Support the team in building, improving, and retraining machine learning forecasting models • Production Operations: Actively assist in monitoring, updating, and troubleshooting forecasting models and pipelines operating in production environments • Data Pipelines & Cleansing: Help build and maintain robust data transformation pipelines using SQLMesh and BigQuery to pre-process large streams of data • Simulation & Validation: Use our internal simulation framework to backtest forecast models and analyze how forecast errors directly impact our high-level EMS optimization yield • Agentic AI & Workflow Automation: Assist in writing, structuring, and testing behaviors for autonomous AI agents to help automate workflows • Documentation & Team Sync: Help maintain clean, clear technical documentation in Notion and collaborate with Optimization and Data Engineers during sprint cycles