Senior Data Scientist II jobs in United States
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LexisNexis · 15 hours ago

Senior Data Scientist II

LexisNexis is a global provider of information-based analytics and decision tools for professional and business customers. The role involves building state-of-the-art research tools, focusing on multimodal document understanding and structured data extraction, while leading the strategy for model collaboration and continuous optimization.

AccountingLegalRisk ManagementSoftware
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H1B Sponsor Likelynote

Responsibilities

Design and iterate the multimodal document parsing pipeline: layout / structural modeling, semantic extraction, cross‑modal alignment, structural reconstruction
Build and optimize a multi‑agent collaboration mechanism: task splitting, parallel / sequential scheduling, peer review, iterative quality improvement loops
Define model selection / composition / routing strategies (dynamic dispatch by document type, structural patterns, quality signals)
Plan and execute model fine‑tuning, domain adaptation, continual learning, active learning, and data feedback loops
Establish end‑to‑end metrics: extraction accuracy, structural consistency, agent collaboration effectiveness, latency, stability, and cost
Build quality assurance and risk controls: drift & anomaly monitoring, confidence estimation, fallback strategies, alignment / compliance checks
Drive mapping and consistency between agent / model outputs and business knowledge field standards

Qualification

Machine LearningDeep LearningMultimodal ModelsPythonStatistical AnalysisDocument UnderstandingData ScienceImage ProcessingAgent CollaborationProblem DecompositionQuality AssuranceResearch Translation

Required

Education: Master's degree or above in a quantitative or technical field (Statistics, Computer Science, Mathematics, Data Science, etc.)
Experience: 5+ years of hands‑on machine learning / data science experience. Proven delivery experience in multimodal (vision + text) or complex document understanding. Practical cases of orchestrating agents (or modular processing logic) in production workflows
Solid foundation in machine learning / deep learning fundamentals, multimodal representations, and cross‑modal alignment concepts
Deep understanding of core principles and common algorithms for multimodal large models: cross‑modal attention & representation alignment, vision/text embedding fusion, hierarchical & layout structure modeling, instruction & contrastive paradigms, long‑context and retrieval‑augmented mechanisms, evaluation and failure mode dissection
Familiar with classic image and signal processing methods: edge & contour detection, filtering & denoising, morphological operations, segmentation & key point feature extraction, frequency / time‑frequency analysis, image enhancement & quality assessment; understands trade‑offs and complementarity with deep features
Knowledge of multi‑agent collaboration patterns: role assignment, task routing, feedback loops, redundancy & cross‑checks. Strong in statistical analysis & experimental design: hypothesis testing, factorial design, power analysis, A/B and multivariate evaluation
Able to decompose complex problems and build metric‑driven optimization paths. Rigorous in data quality & error analysis; rapid bottleneck identification
Ability to translate research pseudo‑code into maintainable, testable Python modules with benchmarking & regression harnesses

Preferred

Designed customization / fine‑tuning of multimodal foundation models, representation learning, or structural understanding subsystems
Built an agent orchestration platform: task decomposition, iterative self‑checks, consensus or voting mechanisms
Experience solving robustness & generalization challenges in large‑scale long documents / heterogeneous layouts
Demonstrated results in cost optimization (model pruning, parameter‑efficient tuning, inference acceleration) or adaptive load scheduling
Publications / patents or open‑source contributions
Demonstrated Python systems optimization (e.g., custom Cython / CUDA kernels, vectorization replacing Python loops, latency reductions in inference pipelines)

Benefits

Annual incentive bonus
Country specific benefits

Company

LexisNexis

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LexisNexis is a data analytics company that provides information solutions and law legal databases to Law and corporate businesses.

H1B Sponsorship

LexisNexis has a track record of offering H1B sponsorships. Please note that this does not guarantee sponsorship for this specific role. Below presents additional info for your reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Represents job field similar to this job
Trends of Total Sponsorships
2024 (9)
2023 (108)
2022 (138)
2021 (87)
2020 (64)

Funding

Current Stage
Late Stage
Total Funding
$30M
Key Investors
EquivoActua Corporation
2021-06-16Corporate Round
2000-03-22Series Unknown· $30M
1994-10-05Acquired

Leadership Team

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Eric Bonnet-Maes
CEO - LexisNexis Continental Europe, Middle East and Africa
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Sean Fitzpatrick
CEO LexisNexis North America, UK & Ireland
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Company data provided by crunchbase