Manager/Sr. Manager - Recommendation Systems

Tiger Analytics is looking for an experienced Leader to join our fast-growing advanced analytics consulting firm. Our consultants bring deep expertise in Data Science, Machine Learning, and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner. We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world.

As a Leader Reccomendation Engineer, you will apply strong expertise through the use of machine learning, data mining, and information retrieval to design, prototype, and build next-generation advanced analytics engines and services. You will collaborate with cross-functional teams and business partners to define the technical problem statement and hypotheses to test. You will develop efficient and accurate analytical models that mimic business decisions and incorporate those models into analytical data products and tools. You will have the opportunity to drive current and future strategy by leveraging your analytical skills as you ensure business value and communicate the results.

Key Responsibilities

• Develop and deploy scalable recommendation algorithms (e.g., collaborative filtering, content-based, hybrid).
• Translate business objectives into data science problems, and deliver solutions that drive measurable outcomes.
• Work with petabyte-scale datasets to train, validate, and optimize ML models (ranking, retrieval, embeddings).
• Build end-to-end ML pipelines (training, validation, CI/CD, deployment, monitoring) using best MLOps practices.
• Collaborate closely with product, engineering, and analytics teams to integrate models into production systems.
• Optimize model inference for latency, scale, and cost-efficiency in production environments. 

Requirements

• 10 years of experience working as a Data Scientist
• Hands-on experience with enterprise data science solutions, preferably in retail, inventory management, or operations research.
• Proficiency in Python, SQL, and PySpark.
• Experience with production-level coding and deployment practices.
• Familiarity with basic machine learning techniques and mathematical optimization methods.
• Proficient in data science libraries and ML pipelines such as; NumPy, SciPy, scikit-learn, MLlib, PyTorch, TensorFlow.
• Self-starter with an ownership mindset and the ability to work with minimal supervision.

Benefits

This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility.