Offer summary

Qualifications:

3–5+ years of experience in ML engineering, data science, or related roles., Proficient in Python, SQL, and data wrangling., Solid grasp of statistical methods and machine learning frameworks., A degree in computer science, mathematics, physics, or data science..

Key responsibilities:

Design, develop, and deploy machine learning models for pricing optimization.

Own the full ML pipeline including data wrangling, model development, and monitoring.

Partner with e-commerce experts to understand revenue and pricing dynamics.

Research and test new algorithms to improve prediction accuracy.

Job description

Profasee - Team: Data Engineering (reporting to CTO)

Who We Are

Profasee is transforming e-commerce with AI-driven dynamic pricing that helps Amazon brands maximize revenue, profitability, and discoverability. Backed by proven founders with multiple exits, we’re on a mission to reinvent how merchants price their products—in real time, at scale.

We’re growing fast and need a Machine Learning Engineer who’s ready to build, experiment, and push the boundaries of what’s possible in pricing optimization.

Who You Are

You’re a builder and problem-solver who thrives on turning raw data into real-world impact. You love taking a business challenge and using ML to crush it. You don’t just write models—you own the full pipeline and make sure it works in production.

You’re curious, hands-on, and unafraid of complexity. You get things done fast and right, and you’re always looking to improve. You know that the best models are the ones that actually deliver results, not just sit in a notebook.

What You’ll Do

Design, develop, and deploy machine learning models that optimize pricing for thousands of products daily
Own the full ML pipeline—data wrangling, feature engineering, model development, deployment, monitoring, and continuous improvement
Partner with e-commerce experts to understand key drivers of revenue, conversion, and pricing dynamics
Research and test new algorithms, statistical methods, and best practices to improve prediction accuracy
Build tools and automation to keep models sharp and adaptive to market changes
Track model performance, spot drifts, and keep improving
Write clean, scalable code and contribute to a strong engineering culture

What You Bring

3–5+ years of experience in ML engineering, data science, or related roles
Proficient in Python, SQL, and data wrangling
Real-world experience building, deploying, and monitoring ML models
Solid grasp of statistical methods and machine learning frameworks
Ability to translate business problems into technical solutions
Comfortable owning the full lifecycle of ML products from R&D to production
Strong analytical mindset and a bias for action
Experience with AWS cloud services
Proven track record taking ML projects from idea to production
Familiarity with libraries like SQLAlchemy, Pydantic, Celery, Scikit-learn, Streamlit, FastAPI, Pandas, object oriented programming and data classes.
A degree in computer science, mathematics, physics, or data science