Job Description
Data Scientist / Machine Learning Engineer
Company: FenixCommerce Location: Noida, India (On-site / Hybrid) Type: Full-time Team: Data Science \& Machine Learning Engineering
About FenixCommerce
FenixCommerce is an AI-powered shipping intelligence platform that helps the world's leading e-commerce brands turn delivery into a competitive advantage. Our products — from accurate estimated delivery dates (EDD) to carrier optimization, ship-from-store routing, and AI-driven operations tooling — run on real-time data at enterprise scale.
Our engineering team in Noida builds the systems behind it: a high-throughput, event-driven platform processing millions of shipments, powering the predictive models that decide what we tell shoppers and how brands move freight. We're looking for a Data Scientist / ML Engineer who wants to own meaningful models end to end — from raw data to production impact.
The Role
This is a hands-on, full-lifecycle role. You'll design and build the data pipelines that feed our models, train and evaluate the models themselves, and ship them to production where they directly shape delivery predictions and carrier decisions for major retailers. You'll work close to the metal of real logistics data — carrier performance, transit times, zone-level patterns, fulfillment behavior — and translate it into models people actually rely on.
What You'll Do
- Build ML data pipelines
- Design, develop, and maintain robust, scalable pipelines for ingesting, cleaning, and transforming large volumes of shipping and order data.
- Train and evaluate models
- Develop, train, tune, and validate ML models for prediction problems such as delivery-date estimation, transit-time forecasting, and carrier performance.
- Deploy to production
- Take models from notebook to production with proper versioning, monitoring, retraining, and performance tracking (MLOps).
- Engineer features
- Work with raw, messy, real-world logistics data to engineer features that materially improve model accuracy.
- Partner across the org
- Collaborate with backend engineers, product, and operations to turn model outputs into reliable, low-latency product capabilities.
- Measure impact
- Define metrics, run experiments, and continuously improve model quality against real business outcomes.
What We're Looking For
- Experience
- Strong hands-on background in data science and ML engineering, with demonstrable experience building data pipelines and training production models.
- Programming
- Proficiency in Python and SQL; comfortable writing clean, production-quality code.
- ML frameworks
- Practical experience with frameworks and libraries such as scikit-learn, XGBoost, PyTorch, or TensorFlow.
- Data engineering
- Experience with large-scale data tooling — e.g., Spark, Kafka, Airflow, or similar — and working with big data in cloud environments.
- Cloud \& MLOps
- Familiarity with AWS (SageMaker, S3, and related services) and modern MLOps practices for deployment, monitoring, and retraining.
- Problem-solving
- A rigorous, metrics-driven mindset and the ability to reason about model trade-offs in a production context.
Preferred Qualifications
- Education
- Preference for candidates who have studied at one of India's top 25 engineering / IT institutions (IITs, NITs, IIITs, BITS, and equivalent).
- Domain
- Exposure to time-series forecasting, predictive modeling, or supply-chain / logistics / e-commerce data.
- Scale
- Experience working with event-driven architectures and real-time data at high volume.
Why Join Us
- Own high-impact models end to end — your work ships to enterprise brands and is measured against real business outcomes.
- Work on genuinely hard, data-rich problems at the intersection of AI and logistics.
- Join a focused, fast-moving engineering team where individual contribution is visible and valued.
How to Apply
Interested candidates should send their profile / resume to
both
of the following addresses to be considered
*
akhilesh@fenixcommerce.com
*
nivedita.nandini@fenixcommerce.com
- We review every application and look forward to hearing from you.