Offer summary

Qualifications:

4+ years of experience in Machine Learning, Data Science, or ML Engineering., Advanced proficiency in Python and SQL, with strong experience in ML libraries like NumPy and Pandas., Solid knowledge of statistical modeling and practical algorithm design., Experience with end-to-end ML pipelines and AWS services such as SageMaker and Glue..

Key responsibilities:

Lead the design, development, and deployment of robust ML systems for business decisions.

Own and evolve core ML infrastructure and pipelines for scalability and reliability.

Collaborate with data engineers and analysts to ensure high data quality and efficient feature pipelines.

Conduct code reviews and mentor ML Engineers and Data Scientists, promoting best practices in model development.

Job description

🚀 Who Are We?

Welcome to Welltech—where health meets innovation! 🌍 As a global leader in Health & Fitness industry, we’ve crossed over 220 million installs with life-changing apps, all designed to boost well-being for millions. Our mission? To improve the health of millions of people through intuitive nutrition trackers, powerful fitness solutions, and personalized wellness journeys—all powered by a diverse team of over 700+ passionate professionals with a presence across 5 hubs.

Why Welltech? Imagine joining a team where your impact on global health and wellness is felt daily. At Welltech, we strive to be proactive wellness partners for our users, while continually evolving ourselves.

About Our Team:
We are the core ML team within a product-focused company. Our mission is to design and deploy impactful machine learning solutions that enhance decision-making and automate key business processes. We work closely with stakeholders across the company and take ownership of end-to-end ML systems — from raw data to deployed models and monitoring.

Our recent work includes:

Building and calibrating LTV prediction models tailored to multiple product verticals.
Researching the relationship between user engagement and monetization using ML tools.
Developing a personalized exercise recommendation system and continuously optimizing it based on user feedback and behavioral data.
Segmenting users through advanced clustering techniques to support product targeting.
Using AI-based models to classify and analyze user reviews across multiple categories.
Improving creative testing through model-driven insights to optimize campaign efficiency.

Required Skills:

4+ years of experience in Machine Learning, Data Science, or ML Engineering.
Advanced proficiency in Python and SQL; strong experience with ML libraries such as NumPy, Pandas, Scikit-learn.
Solid knowledge of statistical modeling, machine learning theory, optimization, and practical algorithm design.
Strong Python coding skills, including writing clean, efficient, and maintainable production code.
Hands-on experience building, deploying, and maintaining ML models in production (not just prototyping).
Experience with end-to-end ML pipelines: data ingestion, feature engineering, training, validation, deployment, and monitoring.
Proficiency in AWS services such as SageMaker, Glue, Redshift, and Lambda.
Familiarity with ML engineering best practices: version control, CI/CD for ML (e.g., model registry, automated testing, retraining pipelines).
Strong communication and stakeholder management skills.
Proven ability to lead complex projects and collaborate with cross-functional teams (e.g., data engineering, product, marketing).

Main Responsibilities:

Lead the design, development, and deployment of robust ML systems that power key business decisions.
Own and evolve core ML infrastructure and pipelines, ensuring scalability and reliability.
Partner with data engineers and analysts to ensure high data quality and efficient feature pipelines.
Design and maintain inference APIs in production environments.
Build internal tools and dashboards using Streamlit to support model transparency and monitoring.
Track model performance, and implement monitoring tools.
Collaborate with business stakeholders to scope ML use cases and translate them into actionable technical solutions.
Conduct code reviews, mentor ML Engineers and Data Scientists, and promote best practices in model development and deployment.
Ensure continuous improvement of deployed models through retraining, feedback loops, and error analysis.

Nice to Have:

Experience with recommendation systems.
Familiarity with reinforcement learning or bandit-based approaches for optimization and personalization.
Knowledge of deep learning frameworks (e.g., PyTorch, TensorFlow).
Familiarity with containerization (Docker).
Exposure to infrastructure-as-code (Terraform) and workflow orchestration (Airflow).

Tech Stack:
Python, SQL, DBT, AWS (SageMaker, Glue, Lambda, Redshift, Spectrum), Docker, Airflow, GitLab, Terraform, Flask, Streamlit

Candidate journey

⭕️ Recruiter call --> ⭕️ Meet a team member --> ⭕️ Skills assessment--> ⭕️ Meet the Leadership team

✨ Why You’ll Love Being Part of Welltech:

Grow Together: Join a culture that champions both personal and professional growth. Here, you’ll thrive as we learn, evolve, and succeed together.
Lead by Example: No matter your role, your leadership matters. Every team member is empowered to inspire and make an impact.
Results-Driven: We’re all about achieving meaningful outcomes. It’s not just about the effort, but the difference we make every day.
We Are Well-Makers: Be part of a movement that’s creating a healthier, happier world. Together, we make well-being a reality!

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