Data Scientist

Position Overview:

Here at ShyftLabs, we are looking 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:

• Data Analysis and Research: Analyzing a large dataset with queries and scripts, extracting valuable signals out of noise, and producing actionable insights into how we could complete and improve a complex ML and bidding system
• Simulation and Modelling: Validating and quantifying the efficiency and performance gain from hypotheses through rigorous simulation and modelling
• Experimentation and Causal Inference: Developing a robust experiment design and metric framework, and providing reliable and unbiased insights for product and business decision making

• Masters degree in a quantitative discipline or equivalent
• 2+ 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, R, or other scripting language, and database language (e.g., SQL) or data manipulation (e.g., Pandas)