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About This Role
Why Socure?
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Socure is building the identity trust infrastructure for the digital economy — verifying 100% of good identities in real time and stopping fraud before it starts. The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day.
We hire people who want that level of responsibility. People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision. If you want predictability or narrow scope, this won’t be your place. If you want to help build the future of identity with a team that holds a high bar for itself — keep reading.
About the Role
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The Big Data R\&D team builds the core entity‑resolution and graph‑based intelligence that underpins Socure’s Verify and KYC products. As a Senior Data Scientist focused on international eKYC, you will be a technical leader driving the next generation of global identity verification solutions. You will design and deploy ML and graph\-based systems tailored to diverse international markets, regulations, and data ecosystems—covering government IDs, telco and credit bureaus, mobile\-first data, and non‑traditional signals.
You will own complex, cross‑product initiatives such as international identity graph evolution, probabilistic matching for non‑US identities, and scalable evaluation frameworks that account for regional regulatory and fairness constraints. You will closely partner with Product, Engineering, Compliance, and GTM teams to launch and scale eKYC solutions across multiple countries and regions.
What You'll Do
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International eKYC Modeling \& Entity Resolution
- Lead the design, development, and deployment of ML and graph\-based algorithms for international entity resolution, identity trust scoring, and anomaly detection across heterogeneous, country‑specific datasets.
- Architect reusable matching and linking frameworks that work across multiple ID schemes (e.g., national ID numbers, passports, voter IDs, mobile accounts, bank accounts) and local name/address conventions.
- Develop probabilistic and rule‑augmented models that handle noisy, sparse, or partially labeled international data while maintaining explainability and regulatory defensibility.
Global Identity Graph \& Data Quality
- Define and evolve the international extension of Socure’s identity graph: schema design, linkage strategies, quality tiers, and confidence scoring that can be leveraged by multiple products (Verify, KYC, watchlists, fraud).
- Design and implement robust data quality and monitoring frameworks for international identity data (coverage, stability, drift, regional bias, label quality) and integrate them into modeling and production monitoring workflows.
- Build scalable approaches for handling linguistic and cultural variation (e.g., transliteration, multi‑script names, address normalization, local naming patterns) in the identity graph and matching pipelines.
Evaluation, Experimentation, and Model Governance
- Own experimentation strategy for major international eKYC initiatives:
- Design offline evaluations and online A/B tests that reflect local ground truth constraints and data sparsity.
- Define success metrics that balance approval rates, fraud capture, and regulatory/operational constraints per market.
- Analyze lift, stability, and fairness trade‑offs and drive go/no‑go decisions with Product and Engineering.
- Define and maintain evaluation frameworks specific to international eKYC (e.g., regional coverage maps, cross‑border identity leakage, local demographic impact, regulatory thresholds).
- Contribute to model governance documentation and support responses to regulators and large enterprise customers regarding model logic, data provenance, fairness, and monitoring for international markets.
Data Source Strategy \& Vendor Evaluation (International)
- Lead the evaluation and integration of international data vendors (e.g., bureaus, telcos, public records, alternative data):
- Design benchmarking methodologies for signal quality, incremental value, stability, and fairness by country/segment.
- Quantify ROI and trade‑offs across multiple vendors and data types; provide clear recommendations that influence product and commercial decisions.
- Partner with Data Acquisition, Legal, and Compliance to ensure that data usage and modeling approaches meet regional regulatory requirements (e.g., GDPR and local privacy/AML/KYC rules).
Technical Leadership \& Cross‑Functional Partnership
- Collaborate with engineering leaders to design scalable, reliable international data and model pipelines using Spark/PySpark, AWS (EMR, S3, SageMaker, Neptune), and modern MLOps workflows.
- Act as a subject‑matter expert on international identity, eKYC regulations, and cross‑border data limitations for internal stakeholders, supporting complex customer questions and strategic roadmap discussions.
- Mentor Data Scientists and Senior Data Scientists on best practices for international modeling: handling low‑label regimes, domain adaptation, localization of thresholds/logic, and building reusable abstractions instead of one‑off country fixes.
- Communicate strategy, progress, and results to senior leadership and cross‑functional partners through clear documents and presentations, framing complex technical work in terms of business impact, regional risk, and regulatory trade‑offs.
What You Bring
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Education \& Experience
- Master’s or Ph.D. in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, or a related field, or equivalent practical experience.
- 6\+ years of hands\-on applied ML / data science experience (4\+ with Ph.D.), including owning production models and pipelines in high‑stakes domains (fraud, risk, identity, payments, credit, or similar).
- Significant prior work on international or multi‑region products is strongly preferred (e.g., cross‑country KYC, credit risk, payments, or compliance systems).
Technical Skills
- Expert‑level proficiency in Python and SQL, with extensive experience in distributed data processing (Spark/PySpark, Databricks or similar) on very large datasets.
- Deep experience designing, training, and deploying models for classification, ranking, anomaly detection, and/or graph learning, including:
- Feature engineering for noisy/heterogeneous identity data.
- Robust evaluation under label sparsity and feedback delays.
- Calibration and thresholding tailored to regional risk and regulatory constraints.
- Proven expertise with graph technologies (e.g., Neo4j, AWS Neptune, GraphFrames, DGL, PyTorch Geometric) and graph algorithms (entity resolution, link prediction, community detection, label propagation) at scale.
*Please note that sponsorship is not available at this time; and that you must be located within 45 miles of a talent hub to be considered.*
*Socure is an equal opportunity employer that values diversity in all its forms within our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.*
*If you need an accommodation during any stage of the application or hiring process—including interview or onboarding support—please reach out to your Socure recruiting partner directly.*
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Compensation Range: $170K \- $200K
Salary Context
This $170K-$200K range is above the median for Data Scientist roles in our dataset (median: $160K across 245 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,133 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Socure, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $198,000 based on 868 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($185K) sits 7% below the category median. Disclosed range: $170K to $200K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Socure AI Hiring
Socure has 6 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span New York, NY, US, Carson City, NV, US. Compensation range: $170K - $300K.
Location Context
AI roles in New York pay a median of $211,000 across 2,760 tracked positions. That's 5% above the national median.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (1,901) and senior (1,663) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $274,200 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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