layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

LAYIQ - Research Data Analyst, DIBI at Northeastern University

Required
Bachelor’s or Master’s degree in Data science, Computer Engineering, Computer Science, Applied Mathematics, or a closely related field.
2–4 years of professional experience in software engineering, data science, or applied R&D, with exposure to machine learning and AI system development in research, prototype, or production environments.
Preferred
Master’s degree with a focus on ML/AI, data ‑ intensive systems, network science, optimization, or related areas.
Experience contributing to government, defense, or security ‑ related R&D programs (internships, fellowships, or full ‑ time roles).
Familiarity with simulation ‑ based models (e.g., physics ‑ based, network ‑ based, agent ‑ based, or stochastic simulations) for analysis, experimentation, or decision support.
Desired Skills & Attributes
Required
Proficiency in Python and familiarity with modern ML/AI development workflows.
Exposure to C++ and/or Java for performance‑critical components is a plus
Experience contributing to the design, implementation, testing, or evaluation of ML/AI‑enabled or simulation‑driven software systems.
Hands‑on experience with machine learning frameworks (e.g., PyTorch), including model training, evaluation, and experimentation.
Familiarity with distributed or accelerated computing environments (e.g., GPU‑enabled systems, shared compute clusters ).
Working knowledge of database systems, including: Relational databases (e.g., PostgreSQL / SQL)
Exposure to graph databases (e.g., Neo4j, Memgraph , or similar)
Familiarity with cloud computing environments (e.g., Azure, AWS, or GovCloud equivalents), including containerized or scalable ML workflows.
Solid software engineering fundamentals, including version control, modular code design, testing, documentation, and reproducibility.
Ability to rapidly prototype solutions and iterate toward more robust implementations with guidance from senior engineers.
Self‑motivated team member able to work independently on well‑defined tasks while contributing to broader project objectives.
U.S. Citizenship with the ability to obtain and maintain a security clearance.
Desired: Exposure to Retrieval‑Augmented Generation (RAG), vector databases, embedding pipelines, or LLM‑enabled systems.
Familiarity with network science or graph analytics concepts, including: Graph modeling and analysis using tools such as NetworkX
Introductory experience with graph‑based ML or GNNs is a plus
Monte Carlo or stochastic simulation methods
Synthetic data generation or simulation‑in‑the‑loop workflows
Exposure to geospatial data, spatiotemporal datasets, or PostgreSQL/PostGIS is a plus.
Interest in UI or frontend development for technical or analyst‑facing tools (e.g., Svelte , React, or similar frameworks).
Familiarity with MLOps concepts, experiment tracking, or reproducible research pipelines.
Experience working with multidisciplinary teams across research, engineering, and applied R&D environments.

Research Data Analyst, DIBI

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