Base Salary Range: $80,000 – $100,000 per year, plus performance bonus and benefits.
Final offer reflects experience, skills, and scope.
Please attach your resume when you apply or drop an email to [email protected]. Applications without a resume may not be reviewed.
About Zenon
Zenon is an AI-native services company. We redesign how enterprises operate — rethinking workflows from first principles and embedding AI where it drives the most value. We build on a solid data foundation, deploy into systems teams already use, and design self-improving systems, because the best version is never the first one. Our clients, including 14 of the top 25 U.S. financial services companies and 12+ private equity firms, have seen significantly lower costs, higher revenues, and improved customer and employee experiences. We're resultants, not consultants: we build and implement, not just advise. Founded in 2018, Zenon is a team leading scientists, architects, and delivery professionals across New York and New Delhi.
The Role
Want to launch your career where AI is actually being put to work? We're looking for a Data Scientist to join our consulting team and drive high-impact analytics and Agentic AI engagements for enterprise clients, primarily within financial services. You'll work alongside senior consultants on live client projects spanning data vendor evaluation, signal and segmentation development, large-scale data pipeline engineering, and LLM- and agent-powered workflow automation. This is a hands-on, client-facing role: you'll write production SQL and Python, help build and maintain analytical pipelines in Snowflake and Databricks, experiment with Agentic AI tooling on real client problems, and contribute rigorous, well-documented analysis that directly informs client decision-making. You'll learn the craft of consulting-grade data science by doing it, with mentorship from senior team members who own these engagements end to end.
What You'll Do
• Write SQL and Python to build, test, and maintain data pipelines that ingest, transform, and validate data from a variety of internal and external sources.
• Support the evaluation, cleaning, and structuring of new datasets — assessing quality, coverage, and fit against a client's specific business questions.
• Contribute to the development of models, scores, and analytical frameworks (e.g., segmentation, propensity, attribution, or risk models) that turn raw data into decision-ready outputs.
• Assist in designing and validating data matching, deduplication, and reconciliation logic to improve accuracy and consistency across datasets and systems.
• Help build and maintain recurring analytical processes and reporting frameworks, with an eye toward reliability, automation, and reproducibility.
• Prepare clear analysis summaries, visualizations, and documentation to support client-facing updates and internal knowledge sharing.
• Explore and apply cutting-edge AI tooling — LLMs, Agentic AI, and multi-agent frameworks — to accelerate analysis, automate manual workflows, and prototype capabilities clients haven't seen before.
• Work across a range of engagement types and industries (e.g., financial services, healthcare, consumer, and beyond), with growing ownership of specific analytical workstreams as you develop.
• Participate in client meetings and internal working sessions, translating technical work into insights stakeholders can act on.
What We're Looking For
• 2–4 years of experience in a data science, data analytics, or data engineering role (internships and academic project experience count).
• Proficiency in SQL and Python for data manipulation, analysis, and pipeline development.
• Familiarity with cloud data warehouses (Snowflake preferred) and/or modern data platforms (e.g., Databricks); willingness to build deep expertise here quickly.
• Foundational understanding of statistics and applied ML methods (e.g., regression, classification, gradient-boosted trees) you're comfortable validating your own work and explaining your reasoning.
• Working knowledge of the broader Python data/ML stack (e.g., Pandas, scikit-learn, PySpark) beyond basic scripting.
• Strong attention to detail and a genuine curiosity about messy, real-world data problems (naming inconsistencies, missing values, non-standard file formats).
• A bias toward quantifiable impact — you think in terms of what a project improved (cost, revenue, accuracy, time saved), not just what it built.
• Clear written and verbal communication skills; comfort presenting findings to non-technical audiences.
• A collaborative, ownership-oriented mindset — you ask questions, follow through, and take feedback well.
• Bachelor's degree in a quantitative field (Data Science, Statistics, Computer Science, Economics, Engineering, or related), or equivalent practical experience.
Nice to Have
• Exposure to large-scale or distributed data processing (e.g., PySpark, Databricks) and performance-oriented pipeline design.
• Experience with entity resolution, record-matching, or data reconciliation techniques.
• Coursework or project experience with applied ML frameworks (e.g., XGBoost, PyTorch) or modern data tooling (dbt, MLflow).
• Familiarity with cloud platforms beyond Snowflake (e.g., AWS, Databricks).
• Prior exposure to financial services, healthcare, or marketing/consumer analytics.
• Exposure to or hands-on experimentation with LLM, GenAI, and Agentic AI tooling (e.g., LangChain, LangGraph, multi-agent frameworks) is a strong plus — we're looking for candidates who are naturally curious and have explored these tools, even in personal projects or experiments, applied to analytical or workflow-automation problems.
• Experience working directly with clients or cross-functional stakeholders in a fast-paced, deadline-driven environment.
Educational Qualification
Bachelor’s or master’s degree in a quantitative field (Data Science, Statistics, Computer Science, Engineering, or related); advanced degree or MBA is a plus.