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About This Role
We Create Products and Brands That People Trust to Clean, Sanitize, and Protect Their Homes and Pools
When you join KIK Consumer Products, you’re joining a team that cares about the work we do and also about each other. We bring exceptional brands and products to consumers that help them protect the health and wellness of their families and the cleanliness of their homes and pools. We are committed to building a culture of performance driven by accountability, collaboration, and agility that enables timely fact\-based decision\-making and exceptional execution with unwavering ethics. As one of North America’s largest independent manufacturers of consumer products, KIK helps a large portfolio of brands and retailers bring their products to life. Your Role at KIK
The KIK Household division is seeking an experienced Director, Data Science and Analytics to support our manufacturing and supply chain organizations. This role reports to the VP,
Supply Chain and partners cross\-functionally to transform complex data into actionable insights that enable both real\-time and trend\-based decision making. In this role you will serve not only as an analytics expert, but also as a translator of data and a problem solver who helps drive operational performance. What You’ll Do
Manufacturing Performance Analytics* Integrate and harmonize data from ERP systems, supply chain reporting, plant systems, and other sources to create a clean dataset for tracking manufacturing performance.
- Develop, maintain, and publish clear, user\-friendly dashboards and visualizations using KIK’s analytics tools (e.g., Qlik, Power BI) to represent manufacturing and end\-to\-end supply chain performance (demand through supply).
- Apply AI and process automation tools to enhance analytics capabilities and support the Household (HH) manufacturing organization’s objectives.
- Partner closely with the VP of Manufacturing to ensure visibility into key data and insights and to address critical plant performance questions.
- Support Workstream Leaders with analytical needs, including sourcing and cleaning data, conducting analysis, and developing new data views to answer business questions.
- Track performance of key initiatives across workstreams, quantify pre\- and post\-initiative results, and identify root causes when expected improvements are not achieved.
- Ensure Maestro reporting accurately reflects program performance and escalate exceptions to leadership.
- Collaborate with the Maestro team to develop and refine dashboards that support data\-driven decision\-making.
- Assist Workstream Leads and Initiative Owners with Maestro questions and troubleshooting.
- Partner with KIK IT to extract data and develop structured data repositories to support reporting and analytics.
- Train KIK associates on dashboard usage and navigation to drive adoption and effective utilization.
Review Cadence \& Leadership Support* Automate reporting used in daily, weekly, and monthly operational reviews.
- Participate in plant and manufacturing project meetings; document actions and update the Maestro tool with new reporting requests, data initiatives, and follow\-ups.
- Act as a data\-driven thought partner to the Household business unit to solve operational problems efficiently.
- Collaborate with the VP of Supply Chain and Senior Manager of Operational Excellence to prepare materials and talking points for bi\-monthly Steering Committee meetings, Household leadership updates, town halls, and other organizational communications
What You’ll Bring* Bachelor's degree required; Master's degree strongly preferred
- Data analytics certifications strongly preferred
- A minimum of 10 years of data science and analytics experience
- Strong executive presence with exceptional interpersonal, communication, and presentation skills; proven success driving enterprise\-level strategic initiatives
- Extensive experience designing and delivering executive\-level data visualization frameworks, KPI governance, and performance reporting
- Proven track record of translating complex data into strategic business insights that drive measurable organizational outcomes
- Deep experience partnering with and influencing C\-suite and senior executive stakeholders across functions
- Expertise managing large\-scale, complex data ecosystems spanning multiple data sources, platforms, and architectures
- Ability to set strategic direction for trend analysis, root cause identification, and enterprise performance management
- Advanced problem solving, critical thinking, and analytical skills with a focus on business impact and ROI
- Preferred: hands\-on or oversight experience with ERP, PowerBI, Smartsheet, Copilot, AI, and RPA tools
- Preferred: Supply Chain and/or Manufacturing consulting experience at a strategic or enterprise level
- Preferred: experience in CPG and/or chemical manufacturing industries
- Preferred: Master's degree and/or advanced data analytics certifications
- Must be able to travel up to 25–30%
What You Will Get
KIK offers a competitive salary and comprehensive benefits including health, wellness, dental, vision, life, and disability insurance. You can plan for your future with KIK's retirement savings options including employer match. KIK also recognizes the importance of continuing education and offers Education Assistance to our employees to encourage continued personal development and growth. About KIK
We create the products and brands that people trust to clean, sanitize, and protect their homes, pools, and cars. We are one of North America’s largest independent consumer product manufacturers with 16 North American manufacturing facilities. We also operate globally in Canada, Europe, South Africa, and Australia. We are known for our portfolio of notable brands including Spic and Span® and Comet® cleaning products, Clorox® Pool\&Spa™ (under license), BioGuard®, and Natural Chemistry® pool chemicals. We are also the \#1 producer in North America of store\-brand bleach and a leading private\-label provider of laundry detergent and additives, dishwashing products, general\-purpose cleaning, and other home care products.
Our global team of over 2,300 employees drives our capabilities in product development, product formulation, strategic sourcing, manufacturing, packaging design, brand marketing, project management, quality assurance, compliance, distribution, and logistics.
Our organization is constantly evolving and is driven by a set of “One KIK” values – a dedication to following through on commitments in a customer\-focused, profit\-motivated way; while never compromising on safety, ethics, or integrity.
KIK is an Equal Employment Opportunity employer. KIK does not discriminate against qualified applicants or employees based on race, color, age, religion, sex, pregnancy, national origin, ancestry, age, physical or mental disability, veteran or uniformed services status, sexual orientation, gender identity or expression, marital status, genetic information or any other status protected by law.
KIK is also committed to providing reasonable accommodations for applicants and employees with protected disabilities to the extent required by applicable laws. If you require a reasonable accommodation to participate in the job application, or interview process, or to perform the essential functions of the job, please contact Human Resources immediately.
Privacy Policy: https://www.kikcorp.com/privacy\-policy\-applicant
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At KIK Consumer Products, 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
KIK Consumer Products AI Hiring
KIK Consumer Products has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Sandy Springs, GA, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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