Applied Data Scientist / Machine Learning Engineer

• Design, develop, and deploy machine learning models that power customer-facing products and business-critical decision-making.
• Partner with Product, Engineering, and Analytics teams to identify opportunities where machine learning can drive measurable value.
• Build and maintain scalable data and feature pipelines to support model training, deployment, and monitoring.
• Develop solutions across forecasting, recommendation systems, optimization, ranking, customer behavior modeling, and predictive analytics.
• Apply statistical modeling, experimentation, and causal inference techniques to solve complex business problems.
• Evaluate model performance, interpret results, and communicate insights and trade-offs to technical and non-technical stakeholders.
• Support MLOps initiatives including model versioning, orchestration, monitoring, drift detection, and retraining workflows.
• Leverage cloud-native technologies and modern data platforms to deploy and scale machine learning systems.
• Explore and implement LLMs, Generative AI, and agentic workflows to enhance product capabilities.

The ideal candidate has a strong foundation in applied machine learning, experience working with large-scale product data, and a proven track record of delivering production-ready ML solutions in SaaS environments., * 5+ years of experience in Applied Data Science, Machine Learning, AI, or ML Engineering.

• Proven experience building, deploying, and maintaining machine learning models in production environments.
• Strong programming skills in Python and advanced proficiency in SQL.
• Hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, Scikit-learn, XGBoost, or similar technologies.
• Deep understanding of machine learning concepts including:
• Supervised learning
• Forecasting
• Recommendation systems
• Ranking algorithms
• Optimization techniques
• Statistical modeling
• Experimentation frameworks
• Experience working with large-scale, real-world datasets and solving complex product challenges.
• Strong background in data engineering concepts, feature engineering, and data pipelines.
• Experience partnering with software engineering teams to deploy, monitor, and improve production ML systems.
• Familiarity with modern data platforms such as Databricks, Snowflake, BigQuery, Redshift, or similar.
• Understanding of MLOps best practices including monitoring, model governance, feature stores, and automated retraining.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform (Google Cloud Platform).
• Strong product mindset with the ability to connect machine learning initiatives to business objectives and customer outcomes.
• Excellent communication and stakeholder management skills., * Experience building ML-powered SaaS products.
• Experience with decision intelligence, workforce optimization, pricing, scheduling, route optimization, marketplace, or operational intelligence solutions.
• Experience with LLMs, Generative AI, Retrieval-Augmented Generation (RAG), or agentic AI applications in production environments.
• Experience designing and analyzing A/B tests, experimentation platforms, or causal inference frameworks.
• Experience operating machine learning systems at scale with feedback loops and continuous improvement processes.
• Previous experience as a Senior, Staff, or Lead Data Scientist, Applied Scientist, or Machine Learning Engineer.