Senior Data Scientist

Job Description

Position Overview

Here at ShyftLabs, we are searching for an experienced Data Scientist who can derive performance improvement and cost efficiency in our product through a deep understanding of the ML and infra system, and provide a data driven insight and scientific solution

ShyftLabs is a growing data product company that was founded in early 2020 and works primarily with Fortune 500 companies. We deliver digital solutions built to help accelerate the growth of businesses in various industries, by focusing on creating value through innovation.

Job Responsibilities:

Research, design, and develop innovative generative AI models and applications.

Collaborate with cross-functional teams to identify opportunities for AI-driven solutions.

Train and fine-tune AI models on large datasets to achieve optimal performance.

Optimize AI models for deployment in production environments.

Stay up-to-date with the latest advancements in AI and machine learning.

Collaborate with data scientists and engineers to ensure data quality and accessibility.

Design, implement, and optimize machine learning algorithms for tasks like classification, prediction, and clustering.

Develop and maintain robust AI infrastructure.

Document technical designs, decisions, and processes, and communicate progress and results to stakeholders

Work with cross-functional teams to integrate AI/ML models into production-level applications

Basic Qualifications:

Master's degree in a quantitative discipline or equivalent.

5+ years minimum professional experience.

Distinctive problem-solving skills, good at articulating product questions, pulling data from large datasets, and using statistics to arrive at a recommendation.

Excellent verbal and written communication skills, with the ability to present

information and analysis results effectively.

Ability to build positive relationships within ShyftLabs and with our stakeholders, and work effectively with cross-functional partners in a global company.

Must have a deep understanding of ML algorithms, ranging from classical methods (e.g., regression, random forests, k-means clustering) to advanced techniques such as gradient boosting (XGBoost, LightGBM, CatBoost), neural networks, and transformer-based architectures (e.g., sentence transformers, BERT variants).

End-to-End Deployment: Proven experience building, training, and deploying ML models from scratch into production environments, including model lifecycle management (versioning, monitoring, and retraining).

Scalability & Performance: Hands-on experience operationalizing models at scale, optimizing for performance, reliability, and cost efficiency in real-world production systems.

Programming: experience with Python or other scripting languages and database language (e.g., SQL) or data manipulation (e.g., Pandas).

We are proud to offer a competitive salary alongside a strong insurance package. We pride ourselves on the growth of our employees, offering extensive learning and development resources.

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