Top Machine Learning Jobs in Little Rock, AR

Develops data quality checks, metadata standards, and AI use-case inventories for HHS. Cleans and integrates complex datasets, applies machine learning and NLP for automated tagging and classification, evaluates AI governance risks, and produces reporting datasets, dashboards, and recommendations. The role requires translating ambiguous federal policy and reporting requirements into repeatable analytical workflows while collaborating with product, design, engineering, and government stakeholders.

Develop and deploy machine learning and LLM-based solutions for cybersecurity challenges. Responsibilities include analyzing complex datasets, building classification and NLP models, optimizing prompting, embeddings, RAG, and fine-tuning techniques, designing evaluation methodologies, and integrating models into scalable production systems. The role requires collaboration with cybersecurity researchers, software engineers, data engineers, and product managers.

Own the authorization optimization product roadmap, improving legitimate transaction approval rates and recovered merchant revenue. Define technical requirements, APIs, data models, integrations, and issuer-specific decisioning strategies. Partner with engineering, data science, sales, solutions, and customer success to deliver fraud-aware authorization improvements involving retries, 3DS, decline recovery, and payment routing. Manage partnerships with payment ecosystem providers, quantify opportunities using issuer and BIN intelligence, and communicate product strategy and performance to stakeholders.

Leads the design, implementation, and support of complex, scalable software components and distributed systems. Provides technical leadership through architecture decisions, mentoring, code and design reviews, troubleshooting, and engineering standards. Partners with product, quality, architecture, and DevOps teams on secure cloud-native solutions, CI/CD, observability, performance, releases, and platform modernization. Participates in hiring and communicates technical risks and tradeoffs to stakeholders.

Leads Parexel’s HEOR modeling service line, overseeing client engagements, project delivery, profitability, business development, team leadership, and strategic growth. Advises senior clients on evidence generation, cost-effectiveness analysis, health policy, and market access. Develops AI-enabled HEOR methodologies using machine learning, natural language processing, analytics, and predictive modeling. Partners with scientists and health economists, represents the organization externally, and manages service-line strategy, budgets, forecasts, revenue, and performance.

Leads biostatistical activities for Phase II–III clinical trials, from protocol design through reporting and regulatory submission support. Responsibilities include statistical analysis planning, sample size calculations, SAP development, TLF review, database lock support, safety reporting, regulatory responses, and oversight of programming or CRO work. Collaborates with cross-functional clinical teams and uses SAS or R for analyses, modeling, simulation, exploratory work, and quality control. Coordinates statistical activities across studies and applies advanced statistical and machine learning methods.

Leads statistical activities for Phase II–III clinical trials from protocol design through reporting and regulatory submissions. Develops statistical analysis plans, sample-size calculations, and reviews tables, listings, and figures. Oversees internal programmers and CRO contributions, applies SAS and R for analyses and quality control, and collaborates with clinical, data management, programming, and regulatory stakeholders. Coordinates statistical activities across studies and applies advanced statistical, machine learning, and modeling methods to support drug development.

Develop and standardize analytical methods for inferring residence, displacement, and poverty from mobile network operator data. Assess data quality, write technical specifications and tests, guide operator engineering teams, tune statistical methods, and balance accuracy with implementation constraints. Design privacy-preserving analyses, improve data security practices, train internal teams, and support international workshops and emergency-response programs.

Resolve complex customer technical issues across Linux, networking, security, Kubernetes, cloud, storage, and ML environments. Diagnose problems, automate workflows with Python and Bash, support model deployment and inference pipelines, communicate resolutions, escalate unresolved issues, and maintain troubleshooting documentation. The role requires rotational shifts or on-call availability and remote work in the USA.

Senior cheminformatics professional responsible for building and maintaining scalable chemical databases, search systems, indexing, similarity analysis, and analytics workflows. The role develops agentic AI solutions for drug and pipeline analysis, integrates scientific literature with computational tools, and applies machine learning to pharmaceutical research and market analytics. Candidates need advanced chemistry or related scientific education, cheminformatics expertise, pharmaceutical or biotech experience, and strong interdisciplinary communication skills.

Own the product roadmap for AI-enabled data products, lead customer discovery with CPG and foodservice stakeholders, translate insights into requirements and priorities, and guide cross-functional delivery from concept through launch. Develop go-to-market and adoption plans, monitor product performance, manage risks, and communicate business impact. The role requires practical AI and machine learning fluency, strong customer research, commercial product management experience, and 20–30% travel.

Develop and own advanced machine learning and AI models using bank transaction and cashflow data to enable financial product capabilities, manage risk, and drive profitable growth. Design experiments, build proofs of concept, monitor production models, and collaborate with data and machine learning engineering teams on scalability. Communicate findings and tradeoffs to business stakeholders while applying statistical, research, and financial services expertise.

Own the strategy, roadmap, architecture, performance, and business outcomes of Versapay’s global payment gateway. Lead processor and acquirer integrations, transaction lifecycle capabilities, compliance, reliability, and payment optimization. Partner with engineering, finance, risk, compliance, operations, and go-to-market teams while guiding execution across engineering pods. Apply AI and machine learning to retry optimization, decline analysis, anomaly detection, incident response, and partner integrations.

Develop AI agents, chatbots, and machine learning models for a modernized federal data science platform. Research and standardize AI capabilities in Databricks and Palantir, prompt-engineer LLMs, integrate AI into DevOps pipelines, and train models on production datasets for prediction and anomaly detection. The role requires collaboration with developers and customers, database familiarity, Agile experience, documentation, and an active or obtainable DoD Secret clearance.

Lead computational platform development for biological design, optimization, and functional prediction. Collaborate with wet-lab scientists to validate predictions, oversee data infrastructure and computational workflows, and contribute to technology roadmaps, funding proposals, and partnerships. The role requires developing machine learning methods for biological sequence and structure analysis, translating biological problems into computational solutions, and leading technical teams across computational and experimental disciplines.

Build and deploy machine learning models that personalize insurance products, analyze product data, design experiments, and measure model impact. The role owns the full data science lifecycle, including data wrangling, production coding, model monitoring, retraining, and experimentation. Responsibilities also include cross-functional collaboration, code reviews, mentoring data scientists, and improving platform practices.

Leads Finance of America's AI security and safeguards program, including strategy, governance, red teaming, model and application security testing, threat detection, monitoring, and control validation. Maintains the enterprise AI inventory, evaluates use cases, integrates security requirements into procurement and development processes, and partners with legal, compliance, risk, and security teams. Reports AI risk posture to executives and boards, supports audits and regulatory examinations, and builds a team while remaining hands-on.

Leads client-facing manufacturing and automotive analytics, data, AI, and digital transformation engagements. Responsibilities include executive stakeholder management, consulting, solution design, account growth, proposal development, commercial oversight, program governance, delivery leadership, and capability building. The role develops transformation roadmaps, modernizes data platforms, guides AI and analytics use cases, manages strategic portfolios up to $10M–$15M annually, and mentors multidisciplinary teams.

Manage provider data operations across onboarding, maintenance, verification, and quality improvement. Monitor offshore teams and automated solutions, develop dashboards and reporting, investigate data issues, and recommend process or tooling improvements. Partner with product, engineering, claims, and data teams to define requirements, test enhancements, quantify business impact, and improve operational scalability. The role also supports AI agents and automation while coordinating multiple initiatives in an ambiguous, metrics-driven healthcare environment.

Lead the development, optimization, and production deployment of advanced machine learning algorithms for advertising effectiveness and ROI. Build scalable ML models, write efficient code, collaborate with machine learning engineers, prototype and test algorithms and data pipelines, and drive iterative improvements using historical data and rigorous performance analysis.

Develop deep learning and AI/ML models to predict cardiac events from ECG and physiological signals. Apply signal processing, analyze implantable-device datasets, build Python tools, validate algorithms, and support feasibility studies for diagnostic features. Collaborate with clinical, software, and hardware teams to improve implantable-device performance and patient outcomes.

Lead Client Legal Services analytics strategy and team development. Own Power BI reporting products, Microsoft Fabric data solutions, governance, predictive modeling, responsible AI adoption, ROI measurement, and executive insights. Partner cross-functionally to improve legal operations, litigation outcomes, staffing, capacity, cost management, quality, and workflow optimization while building scalable analytics capabilities and institutional knowledge.

Conduct independent AI research spanning large language models, reasoning, knowledge acquisition, factuality evaluation, and trustworthy AI. Translate research ideas into scalable prototypes, implement and extend state-of-the-art methods, design experiments, develop algorithms and evaluation methods, use AI-assisted development tools, and communicate findings through technical reports and research papers.

Lead research into novel AI architectures, learning algorithms, reasoning, knowledge representation, and evaluation methodologies. Define and execute ambitious research programs, design experiments, develop large-scale content evaluation systems, mentor research engineers, and communicate findings through technical publications. The role combines scientific leadership with hands-on innovation focused on language models, factuality, evidence quality, source reliability, and trustworthy AI systems.

Conduct advanced research spanning RF communications, digital signal processing, compressive sensing, machine learning, and resilient network systems. Develop and deploy ML pipelines for RF and time-series data using modern frameworks and MLOps practices. Lead projects from concept through demonstration, publish technical findings, and communicate results to stakeholders. Work may support 5G, non-terrestrial networks, cybersecurity, electronic warfare, SIGINT, radar, and other national security applications.