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Data Science Assistant Jobs in New York (NOW HIRING)

TickPick

Data Scientist

New York, NY · On-site

... data science approach to product / feature development, and to drive experiments and initiatives through to a decision and into production. * Assist with analysis of large volumes of transactional ...

TickPick

Data Scientist

New York, NY · On-site +1

... data science approach to product / feature development, and to drive experiments and initiatives through to a decision and into production. * Assist with analysis of large volumes of transactional ...

TickPick LLC.

Data Scientist

Manhattan, NY · On-site

$105 - $135/hr

... data science approach to product / feature development, and to drive experiments and initiatives through to a decision and into production. * Assist with analysis of large volumes of transactional ...

TickPick

Data Scientist

New York, NY · On-site

... data science approach to product / feature development, and to drive experiments and initiatives through to a decision and into production. * Assist with analysis of large volumes of transactional ...

SupportFinity™

Data Scientist (Product)

Manhattan, NY · On-site

$179 - $210/hr

... * Assist with established methods, standard approaches, and promote a data‑informed culture within the organization. What You Bring * Proven expertise in data science, with a strong background in ...

New

Spade Data, Inc.

Data Scientist (Mid-Sr)

Manhattan, NY · On-site

... science and product roadmap with leadership (Senior) Mentor other data scientists and grow the team ... assistants and LLMs are part of how you already work, and you're excited to push further Based in ...

New

campus4tech

Data Engineer

Manhattan, NY · On-site

$126K - $151K/yr

Leverage GenAI/Data Assist tools to accelerate data science workflows. * Collaborate with stakeholders to align models with business outcomes. * Build and refine machine learning models under ...

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Data Science Assistant information

What is a data science assistant?

Data Science Assistants are professionals who support data scientists and analytics teams by handling tasks such as data collection, data cleaning, preparing datasets, conducting preliminary analyses, and creating visualizations. They often work with large datasets, assist in maintaining data integrity, and help automate routine processes. Their role allows data scientists to focus on more complex modeling and analytical work, making the overall workflow more efficient. Data Science Assistants typically have a foundational understanding of statistics, programming (such as Python or R), and data management tools.

What are the key skills and qualifications needed to thrive as a data science assistant?

To thrive as a Data Science Assistant, you need a solid understanding of statistics, data analysis, and programming (often with a background in mathematics, computer science, or a related field). Familiarity with tools like Python or R, data visualization software, and experience with databases or spreadsheet systems are typically required. Attention to detail, strong problem-solving abilities, and effective communication set outstanding candidates apart. These skills are crucial for supporting data-driven decision-making and ensuring accurate, actionable insights for organizations.

How does a data science assistant typically collaborate with data scientists and other team members on projects?

As a Data Science Assistant, you will frequently support data scientists by preparing datasets, conducting preliminary data analysis, and creating visualizations. You will often work closely with analysts, engineers, and subject matter experts to gather requirements and ensure data is cleaned and formatted appropriately. Collaboration is a key part of the role, as you may participate in team meetings, share findings, and help with documentation to keep projects running smoothly. This supportive environment provides an excellent opportunity to learn from experienced professionals and gain exposure to the full data science workflow.

What is the difference between Data Science Assistant vs Data Analyst?

AspectData Science AssistantData Analyst
Required CredentialsBachelor's in Data Science, Statistics, or related fieldBachelor's in Statistics, Mathematics, or related field
Work EnvironmentTech companies, research labs, data-driven departmentsBusiness, finance, marketing, healthcare sectors
Employer & Industry UsageUsed in data science teams for supporting models and analysisUsed across industries for interpreting data and generating reports

While both roles involve working with data, a Data Science Assistant typically supports data science projects, focusing on data preparation and model testing. A Data Analyst primarily interprets data to generate insights and reports. The roles overlap in skills and work environments but differ in their core responsibilities and focus areas.

What are the most commonly searched types of Data Science jobs in New York? The most popular types of Data Science jobs in New York are:
What are popular job titles related to Data Science Assistant jobs in New York? For Data Science Assistant jobs in New York, the most frequently searched job titles are:
What job categories do people searching Data Science Assistant jobs in New York look for? The top searched job categories for Data Science Assistant jobs in New York are:
What cities in New York are hiring for Data Science Assistant jobs? Cities in New York with the most Data Science Assistant job openings:
Data Science Assistant jobs near you
Infographic showing various Data Science Assistant job openings in New York as of August 2026, with employment types broken down into 6% Internship, 79% Full Time, 9% Part Time, 3% Temporary, and 3% Contract. Highlights an 94% In-person, and 6% Remote job distribution.

Data Scientist - Blockchain Intelligence

Merkle Science

New York, NY • On-site, Remote

Full-time

Medical

Re-posted 23 days ago

Job description

About Merkle Science
Merkle Science provides blockchain transaction monitoring and intelligence solutions for web3 companies, digital asset service providers, financial institutions, law enforcement and government agencies to detect, investigate, and prevent illicit use of cryptocurrencies. Our vision is to make cryptocurrencies safe and provide infrastructure for the safe and compliant growth of cryptocurrencies.
Merkle Science is headquartered in New York with offices in Singapore, Bangalore and London. The team has combined experience across Bank of America, Paypal, Luno, Thomson Reuters and Amazon. The company has raised over $27M from SIG, Beco, Republic, DCG, Kenetic, GGV and several others.
About the role
We turn raw on-chain activity into trustworthy intelligence - clustering addresses into real-world entities, attributing them to services and actors, and surfacing risk for compliance and investigations teams. We're looking for a data scientist who is as comfortable shipping a heuristic to production as they are designing it: someone who can move from a messy hypothesis to a working pipeline without waiting on someone else to wire up the data.
You'll work closely with our attribution and clustering leads on models and heuristics that run across billions of transactions and multiple chains (Bitcoin, Ethereum, Tron, Solana, and more).
What you'll do
  • Design, test, and ship clustering and attribution heuristics, and measure them with real precision/coverage metrics rather than vibes.
  • Own your data end to end - pull, clean, join, and model large on-chain datasets without depending on a separate team for every query.
  • Build and maintain the pipelines that take a heuristic from notebook to production, including backfills, incremental runs, and validation.
  • Investigate edge cases (mixers, bridges, exchange hot wallets, consolidation patterns) and translate findings into repeatable logic.
  • Partner with investigations and product to define what "correct" looks like and benchmark against ground truth.
  • Prototype quickly, then harden what works.
What we're looking for
  • 4+ years building data science or data engineering systems that actually shipped (not just notebooks).
  • Strong Python and SQL; comfortable with large datasets and the gotchas of joins, dedup, and skew at scale.
  • Solid grasp of clustering, graph/network analysis, or entity resolution - and a habit of validating results, not just producing them.
  • Ability to reason about precision vs. coverage trade-offs and defend your metrics.
  • Self-directed: you can scope an ambiguous problem, get the data yourself, and drive it to a result.

Our tech stack
You don't need to have used all of these, but here's what you'd be working with day to day:
  • Databricks - our lakehouse and processing backbone. Large-scale on-chain datasets are transformed and modeled here via Spark and SQL; most heuristics run as Databricks jobs against billions of transactions.
  • Kafka - real-time ingestion of on-chain and transaction data. New blocks and events stream in continuously, so a lot of our work is designed to run incrementally rather than as one-off batch jobs.
  • Python - the primary language for everything from exploratory analysis to production heuristics and pipeline code.
  • TigerGraph - our graph database, where addresses, transactions, and entities live as a network. Clustering, traversals, and relationship queries (who funds whom, consolidation paths, entity linkage) happen here.

Supporting cast you'll likely touch:
  • SQL everywhere - for ad-hoc analysis, validation, and defining ground-truth datasets.
  • Columnar / analytical stores (e.g., ClickHouse) for fast aggregate queries over large tables.
  • Orchestration & scheduling for backfills and recurring pipeline runs.
  • Git / GitHub for version control and code review - we expect pipelines and heuristics to be reviewed like any other code.
  • GCP as our cloud environment.
How we work
Small, high-trust team. You'll have a lot of ownership and very little bureaucracy. We prototype fast, measure honestly, and ship.
Well Being, Compensation and Benefits
We care about your well-being. Along with excellent health insurance, we offer flexible time off, learning & development initiatives and hours that are designed to provide work/life balance. We regularly host team-building sessions and encourage discussions around mental health.
We reward talent and believe in acknowledging people for their contributions. We offer industry-leading compensation, along with generous equity. As a rapidly growing business, there are endless opportunities to grow your career with Merkle Science.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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