Data Scientist (SPE)

Shuru

ShuruTech is a rapidly growing B2B IT services company specializing in building scalable, secure, and tailored software solutions for global clients. Our focus spans across CRM development, product engineering, cloud transformation, and automation. We’re passionate about empowering businesses to innovate and grow with custom technology solutions.

We are looking for a skilled Data Scientist with deep expertise in machine learning and artificial intelligence to design and deliver impactful AI solutions spanning large language models (LLMs), recommendation systems, advanced regression and classification techniques, and deep learning architectures - creating actionable insights aligned with Fairness, Accountability, Transparency, and Explainability (FATE) principles.

Lead end-to-end development and deployment of GenAI and machine learning models, including LLMs, transformer architectures, and deep learning solutions.
Design and implement advanced recommendation systems — Search → Match, Search → Match → Rank, content-based, collaborative, and two-tower architectures.
Build data-driven models that address critical business questions and drive strategic, measurable outcomes.
Design and conduct rigorous data analyses with a focus on study design, methodology, algorithms, and statistical modeling.
Build AI solutions aligned with the FATE framework to support fairness, accountability, transparency, and explainability.
Translate complex data insights into strategies aligned with business goals.
Use Python, SQL, and a modern data stack (Azure, Snowflake) to build scalable, efficient solutions.
Use NumPy, Pandas, PyTorch, TensorFlow, and Scikit-learn to deliver production-grade ML models. Partner cross-functionally with engineering, product, and analytics teams; clearly communicate trade-offs,assumptions, and uncertainties to stakeholders.
Establish and champion best practices for shipping ML models to production.

Requirements

Master's degree in Computer Science, Computational Sciences, Data Science, Machine Learning, Statistics,
Mathematics, or another quantitative field.
Deep, hands-on expertise in ML/AI beyond tutorials or side projects.
Proven production experience with LLMs, RAG, transformer architectures, deep learning, XGBoost, CatBoost,
multivariate regression, classification, clustering, and topic modeling.
Experience building recommendation systems, including Search → Match / Search → Match → Rank frameworks
and content, collaborative, and two-tower architectures.
Strong programming skills in Python and SQL; fluency in NumPy, Pandas, PyTorch, TensorFlow, and Scikit-learn.
Proficiency with visualization tools such as Power BI.
Practical knowledge of cloud platforms, particularly Azure and Snowflake.
Demonstrated ability to ship ML models to production and manage deployment/maintenance.
Experience working in both Agile and Waterfall environments.
Self-directed, works effectively with minimal supervision; up to ~10% international travel may be required.

Benefits

Competitive compensation and benefits.
Opportunity to shape technology strategy and business outcomes.
Strong learning and leadership growth opportunities.