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About This Role
Join our IT team at Helen of Troy and make an immediate impact on our trusted brands: OXO, Hydro Flask, Osprey, Honeywell , PUR , Braun , Vicks , Hot Tools, Drybar , Curlsmith , Revlon , and Olive \& June .
Together, we build innovative and useful products that elevate people's lives everywhere every day.
Look around your home, and you'll find us everywhere, in your kitchen, living room, bedroom, and bathroom. We are already making your everyday lives better. We are powered by knowledgeable, enthusiastic, and forward\-thinking people committed to developing a culture of inclusion. Whether you are just starting your career or in need of a challenge, we recognize, develop, and empower talent!
Position: Senior AI Analytics Engineer
Department: IT
Work Location (s):
- El Paso, TX
- Plano, TX,
- Arlington, TN,
- Marlborough, MA.
Hybrid Schedule: At Helen of Troy, we embrace a flexible hybrid work model designed to support collaboration and productivity. For roles eligible for hybrid work, our standard schedule includes in\-office collaboration from Tuesday through Thursday, with the option to work remotely on Mondays and Fridays. Any updates to this model will be communicated in advance. Please note that hybrid eligibility and schedules may vary based on business needs and manager expectations.
What you will be doing:
The Senior AI Analytics Engineer will help drive the adoption and delivery of AI\-enabled analytics solutions across the enterprise. This role combines Microsoft AI technologies, business intelligence, analytics engineering, and modern data platforms to improve decision\-making, automate insight generation, and accelerate business value.
- Design, build, and deploy AI\-enabled analytics solutions using Azure AI Foundry, Copilot Studio, Azure OpenAI, Microsoft 365, Microsoft Graph, and related Microsoft AI technologies.
- Develop AI assistants, copilots, Retrieval\-Augmented Generation (RAG) solutions, Agentic AI workflows, and AI agents that securely leverage enterprise data and knowledge assets.
- Enable AI\-assisted analytics and BI capabilities, including natural language querying, automated insights, narrative reporting, KPI explanations, proactive alerts, and business recommendations.
- Integrate AI solutions with Power BI, semantic models, governed datasets, enterprise APIs, and modern cloud data platforms.
- Utilize Python and modern analytics tooling to prototype, validate, and operationalize AI\-enabled analytics solutions.
- Support advanced analytics initiatives including forecasting, anomaly detection, marketing analytics, social listening, and enterprise insight generation.
- Define and implement AI evaluation frameworks, observability, monitoring, guardrails, security controls, and deployment standards.
- Partner with business stakeholders, BI teams, data engineering teams, and platform owners to identify and deliver high\-value AI and analytics use cases.
- Create reusable solution patterns, technical documentation, and best practices to support enterprise AI adoption and scalability.
Minimum Qualifications:
- Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, Information Systems, Analytics, or a related technical field.
- 8\+ years of experience delivering enterprise analytics, BI, data, cloud, software, automation, or AI solutions.
- 2\+ years of hands\-on AI / GenAI solution delivery experience, including experience deploying enterprise AI solutions into production.
- Hands\-on experience with Azure AI Foundry, Copilot Studio, Azure OpenAI, Microsoft Graph, Microsoft 365, Azure AI Search, or similar enterprise AI technologies.
- Experience building AI assistants, copilots, RAG solutions, semantic search solutions, AI agents, or AI\-enabled business workflows.
- Experience implementing enterprise AI solutions with appropriate governance, monitoring, observability, evaluation frameworks, guardrails, and secure deployment practices.
- Strong proficiency in Python and SQL.
- Experience working with Power BI, semantic models, governed datasets, enterprise reporting platforms, or BI analytics solutions.
- Experience integrating AI or analytics solutions with enterprise data platforms such as Snowflake or comparable cloud data platforms.
- Proven ability to lead technical workstreams and influence solution delivery without direct people management responsibility.
- Ability to travel domestically up to 5%.
Preferred Qualification :
- Master’s degree in a technical, quantitative, or analytics\-related field.
- Highly desired: Experience with Model Context Protocol (MCP), Semantic Kernel, LangChain, LangGraph, agent frameworks, tool\-calling, plugins, or multi\-agent orchestration.
- Experience with Microsoft Fabric, Power Platform, Power Automate, Logic Apps, Azure DevOps, CI/CD, MLOps, LLMOps, or AI observability tools.
- Experience with Jupyter Notebooks, Microsoft Fabric Notebooks, PySpark, Spark, or distributed data processing.
- Working knowledge of statistical modeling, machine learning fundamentals, forecasting, anomaly detection, or model evaluation techniques.
- Familiarity with Snowflake AI capabilities such as Cortex, Cortex Analyst, Cortex Search, Snowpark, Snowflake ML, or Snowflake Intelligence.
- Experience in consumer products, retail, ecommerce, supply chain, marketing analytics, or enterprise BI environments.
Benefits: Salary \+ Bonus , Healthcare, Dental, Vision, Paid Holidays, Paid Parental Leave, 401(k) with company match, Basic Life Insurance, Short Term Disability (STD), Long Term Disability (LTD), Paid Time Off (PTO), Paid Charitable (volunteer) Leave, and Educational Assistance.
Wondering if you should apply? Helen of Troy welcomes people as diverse as our brands! Have the confidence to come as who you are because your point of view, skills, and experience will make us stronger. If you're eager to share new ideas and try new things, we want to hear from you.
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For more information about Helen of Troy, visit www.helenoftroy.com . You can also find us on LinkedIn , and Glassdoor .
*Helen of Troy is an Equal Opportunity/Affirmative Action Employer.* *We are committed to fostering a diverse and inclusive workplace where all employees can thrive. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, pregnancy, genetic information, disability, status as a protected veteran, or any other protected category under applicable federal, state, and local laws.*
*We will provide individuals with disabilities with reasonable accommodations to participate in the job application process. To request an accommodation, please contact Human Resources at (915\) 225\-8000\.*
*Founded in 1968, Helen of Troy is a leading global consumer products company with operations across North America, South America, Europe, and Asia. Our portfolio includes trusted brands such as OXO, Hydro Flask, Osprey, Honeywell, PUR, Braun, Vicks, Hot Tools, Drybar, Curlsmith, Revlon, and Olive \& June – many of which rank among the top brands in their consumer goods categories.*
*We grow and strengthen our brands through innovation, operational excellence, and a collaborative culture focused on delivering exceptional products and experiences for consumers worldwide.*
*The above statements are intended to describe the general nature and level of work performed and are not intended to be an exhaustive list of responsibilities.*
*Management reserves the right to modify the duties and responsibilities of the position at any time.*
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Helen of Troy, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Helen of Troy AI Hiring
Helen of Troy has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Marlborough, MA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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