Data Scientist

Position Overview:

ShyftLabs is seeking an experienced Data Scientist to drive performance improvements and cost efficiencies within our product offerings. This role requires a deep understanding of Machine Learning (ML) and infrastructure systems, enabling the delivery of data-driven insights and scientific solutions. As a growing data product company serving Fortune 500 clients, we are dedicated to creating value through innovation.

Job Responsibilities:

• Data Analysis and Research: Analyze extensive datasets using queries and scripts to extract meaningful signals, identify key trends, and generate actionable insights for enhancing complex ML and bidding systems.
• Simulation and Modelling: Validate and quantify the efficiency and performance gains of hypotheses through rigorous simulation and advanced modelling techniques.
• Experimentation and Causal Inference: Design robust experimental frameworks and metric systems to provide reliable and unbiased insights essential for product and business decision-making.

Basic Qualifications:

• Master's degree in a quantitative field or equivalent practical experience.
• A minimum of 2 years of professional experience in data science or a related role.
• Demonstrated problem-solving abilities, adept at formulating product questions, querying large datasets, and applying statistical methods to derive recommendations.
• Excellent verbal and written communication skills, with the proficiency to effectively present information and analysis findings.
• Proven ability to foster positive relationships internally at ShyftLabs and with external stakeholders, collaborating effectively with cross-functional teams in a global environment.
• In-depth knowledge of ML algorithms, including classical methods (e.g., regression, random forests, k-means clustering) and advanced techniques such as gradient boosting (XGBoost, LightGBM, CatBoost), neural networks, and transformer-based architectures (e.g., sentence transformers, BERT variants).
• Experience in end-to-end ML model deployment, encompassing building, training, and deploying models into production, along with lifecycle management (versioning, monitoring, retraining).
• Hands-on experience in operationalizing ML models at scale, focusing on performance optimization, reliability, and cost efficiency in production systems.
• Proficiency in programming languages such as Python or R, database query languages like SQL, and data manipulation libraries like Pandas.

We are committed to employee growth, offering competitive salaries, a comprehensive insurance package, and extensive learning and development resources.