Senior Data Scientist II

Reed Technology

13d

About The Position

Join our team to help build state-of-the-art research tools. Our Data Science teams focus on extracting key information such as entities mentioned, sentiment analysis, data enrichments, predictive insights, and more to build best in class data and news streams relied on by our global customer base. Responsible for the end‑to‑end design and continuous evolution of a multimodal document understanding and structured data extraction platform: complex PDF / scanned page layout analysis, semantic extraction, structural reconstruction, quality validation, and business integration. Leads multimodal model strategy (vision + language + layout) and multi‑agent collaboration (task decomposition, verification, conflict reconciliation, feedback loops) and plans future customized training and ongoing optimization of models.

Requirements

• 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.
• Capabilities: 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.

Nice To Haves

• 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).

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.

Benefits

• Health Benefits: Comprehensive, multi-carrier program for medical, dental and vision benefits
• Retirement Benefits: 401(k) with match and an Employee Share Purchase Plan
• Wellbeing: Wellness platform with incentives, Headspace app subscription, Employee Assistance and Time-off Programs
• Short-and-Long Term Disability, Life and Accidental Death Insurance, Critical Illness, and Hospital Indemnity
• Family Benefits, including bonding and family care leaves, adoption and surrogacy benefits
• Health Savings, Health Care, Dependent Care and Commuter Spending Accounts
• In addition to annual Paid Time Off, we offer up to two days of paid leave each to participate in Employee Resource Groups and to volunteer with your charity of choice

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 1,001-5,000 employees