Company Overview
DataHat AI is building the next generation of intelligent decision systems for the fashion and retail ecosystem. We help global fashion brands and retailers
optimize demand forecasting, inventory planning, replenishment, and omnichannel fulfillment using a combination of machine learning, deep learning, computer vision, and generative AI.
Our platform goes beyond dashboards and static models—we design agentic.
At DataHat AI, you’ll work at the intersection of real-world supply chain problems, cutting-edge AI, and high-impact business outcomes—with the freedom of a remote-first culture and the ambition of a product-led AI company.
Role Overview
We are looking for a Lead Machine Learning Engineer who can own end-to-end ML systems—from problem framing and feature design to model orchestration, evaluation, and production deployment.
This role is ideal for someone who:
- Loves complex, messy real-world data
- Enjoys blending statistical ML, deep learning, and GenAI
- Thinks in systems and workflows, not just models
- Can translate business problems into scalable, explainable AI solutions
You will play a key role in shaping how AI agents, forecasting models, and optimization engines are built and deployed across fashion retail, physical stores, and e-commerce.
What You’ll Work On
Forecasting That Actually Works
- Build
real-world demand forecasting systems
for fashion using ML, deep learning, and statistical models.
- Tackle messy problems like
intermittent demand, stockouts, seasonality, and cold starts
across stores and e-commerce.
- Design
segmented and ensemble models
that balance accuracy, stability, and business risk.
Data/Features Engineering, Not Guesswork
- Design semantic data models that encode business meaning, hierarchies, and relationships
- Create
high-signal features
from sales, pricing, inventory, calendars, weather, and store performance.
- Set
data quality and imputation rules
that prevent leakage, bias, and false insights.
- Make models
explainable and trustworthy
, not black boxes.
Agentic AI in Production
- Build
AI agents
that reason, plan, and critique forecasts and replenishment decisions.
- Use
LLMs to orchestrate workflows
, apply business constraints, and explain outcomes in plain language.
- Turn model outputs into
actionable decisions
, not just numbers.
Vision-Powered Fashion Intelligence
- Use
computer vision
to extract visual signals from product images—similarity, style, warmth, and more.
- Solve
cold-start and substitution problems
with visual embeddings.
- Go beyond transactional data to understand
why products sell
.
Ship ML That Is Secure and Scales
- Own models from
idea → production → monitoring
.
- Build systems that are
fast, reliable, and battle tested
.
- Build systems that are
secure, observable and auditable.
- Work closely with product and engineering to turn AI into
measurable business impact
.
What We’re Looking For
Strong ML Foundations
- 7–10 years of hands-on experience building
applied ML systems
(not just experiments).
- Deep understanding of
statistical ML, time-series forecasting, and model evaluation
.
- Fluent in
Python
and the modern data stack (Pandas, NumPy, scikit-learn).
Modern AI Builder Mindset
- Experience with
deep learning and Transformers
—and strong judgment on
when not to use them
.
- Hands-on exposure to
Generative AI, LLMs, and agentic AI patterns
(tool use, planning, critique).
- Hands-on experience with frameworks like Langchain, LangGraph, AutoGen etc.
- Comfortable blending
ML + rules + reasoning
to solve real business problems.
Computer Vision & Representation Learning
- Experience working with
image embeddings, similarity search, and clustering
.
- Ability to extract meaningful signals from
product images
(style, structure, visual similarity).
- Fashion or retail CV experience is a strong plus.
Systems & Domain Thinking
- Ability to reason about
end-to-end systems
, not isolated models.
- Experience working on
retail, fashion, supply chain, or e-commerce
problems (preferred).
- Experience working with
large, noisy, real-world datasets
and imperfect signals.
- Strong intuition for
constraints, trade-offs, and operational realities
.
Leadership, Ownership & Impact
- Demonstrated ability to
own ambiguous problems
, define success metrics, and drive solutions to production.
- Strong communication skills—able to explain complex models, assumptions, and limitations clearly to technical and non-technical stakeholders.
- Track record of influencing
product, engineering, and business decisions
through data and models.
- Bias toward
shipping, learning, and iterating
over perfect theory.
- Mentorship mindset and willingness to raise the technical bar of the team.
Why Join Us
- Solve real, high-impact business problems with AI
- Work on GenAI + Agents + ML + CV in production, not just experiments
- Build end-to-end systems, not isolated models
- Fully remote-first, outcome-driven culture
- High ownership, high learning, high impact