← Back to your search

Quantiphi

Senior Machine Learning Engineer - Traditional

Data & ML

Employment

Full-time
From the employer

Full time

Level

Senior

Category

Data & ML
Not doable from Austria

Country assessment

Not doable from Austria because it is tied to an office and never says it can be done remotely.

Our assessment is guidance. Confirm arrangements with the employer.

Skills mentioned in this posting

PythonAWSML/AI

Job description

While technology is the heart of our business, a global and diverse culture is the heart of our success. We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth.


If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi!

JOB ROLE - ML Engineer

As an  ML Engineer This role focuses on predictive modeling development utilizing Jupyter Notebook environments alongside AWS cloud infrastructure services.

Must have Skills:

  • Must be capable of working independently with minimal supervision alongside the client's business/technical team

  • 4+  years experience in Traditional Machine Learning & Predictive Modeling: Hands-on experience building and fine-tuning ML models for regression/classification tasks, specifically sales forecasting or growth prediction using time-series and tabular data

  • Python & ML Libraries: Strong command of Python with libraries such as Scikit-learn, Pandas, NumPy, and Jupyter Notebooks for model development and output presentation.

  • Model Testing and evaluation

  • Feature Engineering & EDA

  • AWS Data Ecosystem: Working knowledge of AWS S3 and Amazon Redshift for data ingestion, storage, and retrieval in a cloud development environment

  • Data Preparation & Quality: Experience in data handling, missing values, duplicates, inconsistencies, and building unified analytical datasets from multiple sources

Good to have skills:

  • Store/Retail Domain Knowledge: Understanding of retail KPIs, store segmentation frameworks, and business cockpit/reporting concepts

  • Stakeholder Communication: Ability to present model outputs, performance metrics, and insights to non-technical business stakeholders during weekly review cadences

If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!