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Mgr Software Engineering

Job in Alpharetta, Fulton County, Georgia, 30239, USA
Listing for: LexisNexis Risk Solutions
Full Time position
Listed on 2026-05-31
Job specializations:
  • IT/Tech
    AI Engineer, Data Science Manager, Data Analyst, Data Security
Salary/Wage Range or Industry Benchmark: 115400 - 192300 USD Yearly USD 115400.00 192300.00 YEAR
Job Description & How to Apply Below

About the Business

Lexis Nexis Risk Solutions is the essential partner in the assessment of risk. Within our Business Services vertical, we offer a multitude of solutions focused on helping businesses of all sizes drive higher revenue growth, maximize operational efficiencies, and improve customer experience. Our solutions help our customers solve difficult problems in the areas of Anti‑Money Laundering/Counter‑Terrorist Financing, Identity Authentication & Verification, Fraud and Credit Risk mitigation and Customer Data Management.

You can learn more about Lexis Nexis Risk at

About the Team

This position provides leadership, management, direction, and vision to data engineering and/or employees including offshore contractors/consultants and interns to oversee statistical and analytical data analysis. The position works closely with technology peers, product and project leaders/managers, and directs the successful completion and delivery of respective data components and related deliverables. The position additionally reports progress to senior management. Additional responsibilities include oversight of the department budget, identification and support of talent, and definition of resource requirements and allocations.

About

the Role

This role leads the Analytics Engineering function with a focus on embedding AI into delivery while ensuring strong Info Sec and data governance standards. It sits at the intersection of engineering, AI, and risk—driving practical adoption of AI in analytics workflows without compromising compliance or control.

Responsibilities
  • Team Leadership & Delivery:
    Lead and support analytics engineers, ensuring clear priorities and consistent delivery; remove blockers, drive accountability, and maintain pace across initiatives.
  • AI Adoption in Analytics:
    Identify and prioritize high‑impact AI use cases (e.g. LLM‑driven parsing, enrichment, copilots); embed AI into pipelines and workflows—not as experiments, but as part of production delivery; guide teams on practical implementation and evaluation of AI outputs.
  • Info Sec & Governance:
    Ensure all AI and data usage aligns with internal Info Sec, privacy, and regulatory requirements; define and enforce standards for safe AI usage (e.g. PII handling, approved models, access control); work closely with Info Sec and architecture teams on risk management and approvals.
  • Data Quality & Control:
    Establish validation frameworks for AI outputs (e.g. benchmarking, HITL, drift monitoring); maintain strong data governance, lineage, and quality across analytics assets.
  • Stakeholder Engagement:
    Partner with product, business, and platform teams to align priorities and use cases; translate business needs into scalable analytics and AI solutions.
Requirements
  • Bachelor’s Degree in Engineering/Computer Science or equivalent experience required; advanced degree preferred.
  • Proven experience in data and scoring engineering, data management, and data strategy with technical knowledge along with management experience.
  • Strong background in analytics engineering/data engineering (SQL, data modelling, modern data platforms).
  • Experience applying AI/LLMs in production use cases, not just experimentation.
  • Solid understanding of data governance, Info Sec, and regulatory considerations (PII, model risk, access control).
  • Proven experience leading teams and driving delivery in a fast‑paced environment.
  • Ability to balance innovation with control—moving quickly without introducing risk.
  • Strong communication skills across both technical and business stakeholders.
  • Nice to Have:
    Experience with platforms such as Databricks, Synapse, Power BI; familiarity with agent‑based workflows, prompt engineering, and evaluation frameworks; exposure to regulated environments or sensitive data domains.
Success Measures
  • AI capabilities embedded into core analytics workflows.
  • Clear governance model for AI usage adopted across the team.
  • Improved delivery pace and reduced rework through better coordination.
  • High trust in data and AI output from business stakeholders.
Working for You (Benefits)
  • Medical Inpatient and Outpatient Insurance:
    Coverage for your healthcare needs.
  • Life Assurance Policies.
  • Modern…
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