Top Entry Level Data Science Jobs in NYC, NY

Build and own Legora’s Growth data foundation, including trusted dbt models, data pipelines, shared GTM metrics, dashboards, and data activation into business systems. Partner with Marketing, Sales, RevOps, and Customer Success to analyze funnels, attribution, pipeline, retention, expansion, and customer behavior. Improve data quality, conduct business deep dives, and establish scalable data-team standards and tooling.

Develop advanced Bayesian models and stochastic simulations for business modeling, product innovation, user behavior, and system performance. Manage analytical projects end to end, including data preparation, model development, validation, deployment, and proof-of-concept applications. Communicate methodologies, progress, and findings through reports and stakeholder presentations.

Develop partner-specific pricing strategies for products and bundles, analyze volume, conversion, cost, and competitive data, and identify revenue opportunities. Build AI-powered pricing calculators, deal-sizing models, dashboards, and experimentation frameworks. Collaborate with Finance, Partnerships, Product, Compliance, and Legal to ensure commercially sound pricing. Establish scalable pricing frameworks, reduce escalations, document decisions, and communicate complex pricing recommendations to senior stakeholders and non-technical partners.

Build and validate forecasting, optimization, anomaly detection, and measurement models using complex customer data. Perform exploratory analysis, experiments, backtesting, and error analysis; partner with ML and data engineers to productionize models, features, evaluation pipelines, and monitoring. Translate business questions into analytical recommendations and communicate findings to technical and non-technical stakeholders. The role is hybrid in New York City or remote in the U.S. with monthly travel to New York.

Analyze product and operational data to guide roadmap decisions, define metrics, evaluate experiments, uncover user friction, and build reliable dbt models, dashboards, and analytical tools. The role partners closely with Product and Operations, improves data quality and instrumentation, and translates complex findings into actionable recommendations. It requires strong SQL, Python, experimentation, data modeling, visualization, and product judgment skills.

Develop product analytics and experimentation for Spotify’s Discovery Mode promotion tools. Build metrics, instrumentation, dashboards, and actionable insights to improve customer engagement and business outcomes. Collaborate with product, engineering, research, design, marketing, and other data science teams to identify opportunities, test hypotheses, and influence product decisions through clear data storytelling.

Develop predictive models and machine-learning solutions for cybersecurity, payment fraud detection, and vulnerability analytics. Analyze large datasets using distributed processing, identify patterns, create technical rules, integrate cybersecurity and transaction data, and design performance metrics. Collaborate with product, engineering, and operations teams, translate stakeholder needs into analytical solutions, and communicate insights to leadership. The role also involves graph databases and graph machine learning, including graph neural networks.

Develop and evaluate foundational deep learning methods, including transformers, embeddings, and neural networks, for credit modeling and loss prediction. Integrate models into ML pipelines, apply representation learning to financial data, and build specialized AI agents for data science and business workflows. Collaborate with technical and product teams, communicate findings, mentor colleagues, and measure model impact on customer and business outcomes.

Create reproducible data science and machine learning tasks with verified reference solutions, robust tests, and evaluation criteria. Investigate coding-agent failures, identify implementation errors and data leakage, refine task designs, and document requirements and verification steps. The role requires competitive data science experience, mathematical reasoning, programming ability, and practical expertise in implementing and validating solutions.

Analyze large datasets, identify meaningful patterns, develop machine learning models, and deliver actionable insights for business decisions. Collaborate with cross-functional teams to understand data needs, communicate findings clearly to stakeholders, and stay current with advances in data science and AI technologies.

Supports the design, validation, and operationalization of predictive, machine learning, and AI models. Analyzes structured and unstructured data, develops algorithms, validates outcomes, creates visualizations, and communicates findings to stakeholders. Collaborates cross-functionally to integrate solutions and prepares model pipelines for business use. The role also involves statistical analysis, NLP, exploratory data analysis, feature engineering, and research into emerging technologies.

Designs challenging data science and machine learning tasks, builds reproducible environments and verified reference solutions, and develops automated tests and evaluation frameworks for AI-generated code. The role analyzes AI agent performance, identifies bugs, leakage, overfitting, and methodological weaknesses, documents technical requirements, and iteratively improves task reliability and complexity.

Conduct research on how consumer LLMs search, reason, and synthesize information; design experiments and causal inference analyses; support product and engineering decisions with rigorous measurement; and build data pipelines and recurring evaluations that turn evolving research findings into product capabilities.

Build predictive and machine learning models for patient needs, operational forecasting, churn, advocate quality, and risk. Design A/B tests and causal inference studies, apply advanced statistical methods, productionize models with data engineers, and communicate insights to business stakeholders. The role requires Python, advanced SQL, forecasting expertise, statistical proficiency, data privacy awareness, and the ability to work independently in a fast-paced healthcare startup.

Supports equity research analysts by querying, transforming, analyzing, and combining large datasets; developing statistical models; and building AI tools, agents, MCP servers, and data processes. Ensures datasets meet accessibility, quality, lineage, and governance standards. Translates technical methods into actionable insights for nontechnical stakeholders while collaborating with research teams, vendors, management, and clients.

Supports equity research analysts by querying, transforming, analyzing, and combining large datasets; developing statistical models; building AI agents, MCP servers, and data workflows; and ensuring data quality, lineage, governance, and AI accessibility. The role translates complex technical methods into actionable insights, collaborates with research teams and vendors, and contributes to generative AI tooling and platform enhancements.

Partner with AI labs to improve frontier models by designing targeted data collection workflows, analyzing experimental results, and implementing quality controls. The role involves evaluating model behavior, reducing bias, supporting model safety and alignment, and translating technical findings into actionable insights for AI researchers and customers. Python is used for data analysis, pipeline development, and workflow prototyping.

Own Alpaca’s growth data strategy across acquisition, activation, retention, referral, and monetization. Build experimentation frameworks, causal and incrementality analyses, funnel and cohort models, LTV/CAC and churn forecasts, marketing measurement, governed growth metrics, and self-serve analytics. Partner with Product, Marketing, Sales, Engineering, and Finance to influence strategy and roadmaps, communicate insights to leadership, mentor data professionals, and establish standards for growth analytics.

Build AI, machine-learning, and statistical models to predict clinical-trial outcomes, regulatory results, and commercial potential. Analyze trial designs, endpoints, patient populations, treatment effects, safety, and subgroup data. Apply generative AI, LLMs, and NLP to scientific and regulatory information. Develop pipeline-monitoring tools, validate models, control bias and data leakage, and translate clinical and quantitative findings into investment conclusions supporting equity research, forecasts, valuations, and published analysis.

Assist senior data scientists with data collection, preprocessing, cleaning, and basic statistical analysis. Support model development, perform basic visualizations, and document processes and findings while learning and applying data science techniques.

As a Data Scientist, you will collaborate with scientists to develop algorithms and software solutions for data analysis and machine learning, while engaging in multidisciplinary teamwork to meet project objectives.

Partner with product engineering to design and analyze experiments, apply causal reasoning and statistical modeling to marketplace data, and drive data-informed product decisions across the engineering org.