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Company
“A perfume is a world you can return to over and over again–a keyhole into far off places–material and imaginary.”
D.S. & Durga is a Brooklyn fragrance house on a quest to create thrilling perfumes inspired by music, myth, and the mundane. Founded in 2009 by David (D.S.) and Kavi (Durga) Moltz, the brand is known for its uniquely distinguished perfumes, candles, and aromatic products. All fragrances and design worlds are original compositions, rooted in specificity, surrealism, and clarity.
With distribution across 600 doors globally and five freestanding stores in NYC and LA, the brand is entering a new phase of growth following Manzanita Capital’s majority investment in 2024.
Role Overview
D.S. & Durga has built a loyal following through creative vision, and its next chapter depends on sustainable growth grounded in a clear, unified understanding of the customer — something the business has never had. Right now, customer data exists everywhere and is owned nowhere: DTC, retail, social, and commercial teams each see a different slice of the picture, and major decisions about products, launches, investments, and channels get made without a shared view of who the customer actually is. The Manager, Customer Intelligence & AI exists to close that gap, building a trusted, fact-based customer intelligence function rather than a reporting role. It reports to the CMO to protect its objectivity, while partnering closely with the Senior Manager, Global Marketing & Innovation to prioritize where that intelligence gets applied and embed it into campaign, innovation, and pricing decisions.
This person will build and own D.S. & Durga's customer intelligence capability from the ground up, working across marketing, commercial, retail, and the creative studio to connect, interpret, and make useful the data that already exists — not to collect it, but to unify it into a single actionable picture, in partnership with the teams (social, web, DTC, retail) who own the underlying sources. The role unfolds in two phases: first building the internal customer view, SKU-level data infrastructure, and core reporting; then expanding into brand and market intelligence, including competitive benchmarking, share of voice, and cultural trend monitoring. As a single-person function from the outset, the role requires genuine LLM fluency — training, fine-tuning, and directing models and agents to do the work of a team that doesn't exist yet, making automation and AI-assisted analysis the foundation of the function's credibility.
Key Responsibilities
Phase One — Customer & Business View* Build one unified, omni-channel customer view based on real purchasing behavior, basket data, lifetime value, frequency, channel preference, and product affinity
- Deliver SKU-level intelligence — what's selling, what isn't, where, at what price point, in which channel, and why
- Own monthly dashboard and reporting, giving leadership, commercial, retail, and marketing a consistent view of business performance, customer behavior, and emerging trends
- Integrate social, web, DTC, and retail data sources into a single intelligence framework
- Develop customer segmentation that reflects both target audience strategy and data reality, informing innovation priorities, marketing investment, and commercial planning
- Train and direct AI models and agents to automate reporting, accelerate analysis, and surface insight faster — giving the function the leverage of a small team while it remains one person
Phase Two — Brand & Market Intelligence* Track brand health — awareness, consideration, sentiment, and share of voice across key markets
- Lead competitive benchmarking and category trend monitoring
- Monitor AI visibility — how D.S. & Durga appears in AI-generated search and recommendations
- Partner with Global Marketing on consumer research initiatives — surveys, panels, qualitative studies
- Track cultural trend signals relevant to the brand's world — music, travel, fragrance, design, literature
Cross-Functional Partnership* Serve as a connective force across marketing, commercial, retail, and the creative studio without owning every underlying data source
- Partner day-to-day with the Senior Manager, Global Marketing & Innovation to prioritize where intelligence is applied and embed it into campaign, innovation, and pricing decisions
- Maintain independence and objectivity so insights are trusted as unfiltered, fact-based signal rather than shaped by any one function's priorities
Competencies: To perform the job successfully, an individual should demonstrate the following:
Achievement Focus — Demonstrates persistence and overcomes obstacles. Measures self against a standard of excellence. Sets and achieves challenging goals, especially as the sole owner of a new function.
Analytical Rigor — Moves comfortably between a data model and a leadership presentation. Understands that insight is only as valuable as the decision it changes.
Continuous Learning — Builds fluency in AI tools and models on an ongoing basis. Trains, fine-tunes, and directs agents rather than waiting for headcount.
Communications — Expresses ideas clearly in written and verbal form. Brings people along when asking them to share data or change how they work.
Planning & Organization — Prioritizes across two phases of work. Builds infrastructure and reporting systems from the ground up in a resource-constrained environment.
Problem Solving — Makes something from nothing. Gathers and connects data across disparate, unowned sources to produce a coherent, actionable picture.
Teamwork — Operates as a shared resource across marketing, commercial, retail, and the creative studio. Balances independence with close cross-functional partnership.
Use of Technology — Adapts to and directs AI models and agents as core working tools. Treats automation as the foundation of the function's credibility, not a supplement to it.
qualifications: To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
Education/Experience:* 5 to 8 years of experience in consumer insights, customer analytics, business intelligence or a closely related field.
- Experience in prestige beauty, fragrance, luxury or a high-growth consumer brand — in-house or at a leading insights or strategy consultancy.
- Strong command of both quantitative and qualitative approaches to understanding consumers.
- Proficiency with analytics platforms, CRM systems and data visualization tools.
- Genuinely LLM-efficient: hands-on experience training, fine-tuning, prompting and directing models and agents to do real analytical and operational work — not just familiarity with AI tools at a surface level.
- Experience presenting insights to senior leadership and translating data into clear strategic recommendations.
- Comfort operating as a shared resource across multiple functions, with a dotted-line relationship to a function head.
- Experience building reporting infrastructure and dashboards from scratch is a strong plus.
- Experience standing up a function solo — comfortable being a department of one until the business case for a team is proven — is a strong plus.
Language Ability:
Read, analyze and interpret business, professional, technical or governmental documents. Write reports, business correspondence and procedure manuals. Effectively present information and respond to questions from managers, customers and the public.
Mathematical Ability:
Add, subtract, multiply and divide in all units of measure, using whole numbers, common fractions and decimals. Compute rate, ratio and percent, and draw and interpret bar graphs.
Reasoning Ability:
Solve practical problems and deal with a variety of concrete variables in situations where only limited standardization exists. Interpret a variety of instructions furnished in written, oral, diagram, or schedule form.
Computer Skills:* MS Office, comfort with AI/LLM tools, model training and fine-tuning, and directing agent-driven workflows, familiarity with data visualization and reporting platforms
Benefits:* The salary range for this role is $95,000-$120,000 dependent upon experience
- Location: Brooklyn, NY (Hybrid- 3 days per week in office)
- Full Time employees are eligible for medical, dental and vision insurance, employer paid life insurance, 401k plan, pre tax commuter benefits, quarterly gratis, employee discount, long-term & short- term disability leave and much more.
Supervisory Responsibilities: This job has no supervisory responsibilities.
Work Environment: The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of this job. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions.
The noise level in the work environment is usually moderate.
Physical Demands: The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions.
While performing the duties of this job, the employee is regularly required to sit. The employee is frequently required to use hands to finger, handle, or feel and talk or hear. Specific vision abilities required by this job include close vision.
The above job description is not intended to be an all-inclusive list of duties and standards of the position. Incumbents will follow any other instructions, and perform any other related duties, as assigned by their supervisor.
D.S. & Durga is an Equal Opportunity Employer. We do not discriminate on the basis of race, color, creed, religion, gender (including gender identity or expression), pregnancy, childbirth or related medical conditions, sexual orientation, marital status, domestic violence, sexual violence or stalking victim status, ancestry, national origin, alienage or citizenship, age, disability, military or veteran status, genetic information or predisposing genetic characteristic, or other protected status.
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Salary Context
This $95K-$120K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At D.S. & Durga, 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 in Demand for This Role
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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($107K) sits 49% below the category median. Disclosed range: $95K to $120K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
D.S. & Durga AI Hiring
D.S. & Durga has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Brooklyn, NY, US. Compensation range: $120K - $120K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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