Senior Data Engineer - Machine Learning

Employment Type: Full-Time Location Type: Remote Salary: Not Specified

🚀 Who Are We?

Welcome to Welltech—where health meets innovation! 🌍 As a global leader in the Health & Fitness industry, we’ve crossed over 220 million installs with three 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 750+ 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.

What We're Looking For

We are looking for an experienced Senior Data Engineer to join the core Machine Learning 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.

Key Responsibilities:

• Design and deploy ML models that support critical business functions such as LTV prediction, user classification, personalization, and content tagging.
• Analyze product and marketing performance using statistical and ML techniques to support data-driven decision-making.
• Develop models for processing unstructured data: user reviews, feedback, support tickets (NLP), and creative assets (computer vision).
• Contribute to the design and evolution of content recommendation systems and personalization logic.
• Build internal tools and dashboards to increase model transparency and accessibility for business teams.
• Monitor model performance and support the full model lifecycle: retraining, error analysis, version control.
• Collaborate with data engineers to validate new features, contribute to pipeline standardization, and support data readiness for production use.
• Provide analytical support to marketing teams: metric design, experiment planning, evaluation, and modeling impact.

Required Skills:

• 4+ years of experience in Data Science or product analytics involving machine learning.
• Strong proficiency in Python and SQL; hands-on experience with core DS/ML libraries such as NumPy, Pandas, Scikit-learn, and XGBoost.
• Solid understanding of statistics, experimental design (A/B testing, uplift modeling), and causal inference.
• Proven experience developing, evaluating, and interpreting ML models for real business problems — including classification, regression, recommendation systems, NLP, and computer vision.
• Practical experience building and maintaining end-to-end ML pipelines: data preparation, feature engineering, training, validation, deployment, and monitoring.
• Hands-on experience with AWS services, especially SageMaker, Glue, Redshift, and Lambda.
• Familiarity with MLOps practices: model versioning, CI/CD, automated retraining, and monitoring.
• Strong collaboration skills and the ability to work closely with product managers, analysts, and engineers.
• Experience analyzing user behavior, running segmentation, and evaluating marketing or product experiments.

Nice to Have:

• Experience with recommendation systems or personalization.
• Background in Marketing Science: media mix modeling (MMM), attribution models, ROI estimation, uplift modeling.
• Applied experience in NLP or computer vision (e.g., content classification, creative decomposition).
• Familiarity with reinforcement learning or contextual bandits for optimization and personalization.
• Experience with Docker, Airflow, and Terraform.
• Interest in model interpretability and explainability tooling.

Tech Stack:

• Python, SQL, DBT, AWS (SageMaker, Glue, Lambda, Redshift, Spectrum), Docker, Airflow, GitLab, Terraform, Flask, Streamlit.

Candidate Journey:

1. ⭕️ Recruiter call
2. ⭕️ Meet the hiring manager
3. ⭕️ Meet the leadership team