About ShyftLabs
ShyftLabs is a fast-growing data product company founded in early 2020, working primarily with Fortune 500 clients. We design and deliver cutting-edge digital and data-driven solutions that help businesses accelerate growth, improve decision-making, and create measurable value through innovation.
Position Overview
We are seeking an experienced Data Scientist who can drive performance improvements and cost efficiencies in our products through a deep understanding of machine learning (ML) and infrastructure systems. In this role, you’ll provide data-driven insights and scientific solutions that directly influence our product strategy and business outcomes.
-
Data Analysis & Research: Analyze large datasets using queries and scripts to extract meaningful insights and identify opportunities for improving complex ML and bidding systems.
-
Simulation & Modelling: Design and execute simulations to validate hypotheses, quantify efficiency gains, and model system performance.
-
Experimentation & Causal Inference: Develop robust experiment designs and metric frameworks to deliver unbiased, data-backed insights for product and business decisions.
-
End-to-End ML Deployment: Build, train, and deploy ML models into production environments, managing the full lifecycle including versioning, monitoring, and retraining.
-
Scalability & Performance Optimization: Operationalize ML models at scale, optimizing for performance, reliability, and cost efficiency in real-world production systems.
-
Cross-Functional Collaboration: Work closely with product, engineering, and data teams to translate business problems into analytical solutions.
-
Master’s degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Data Science) or equivalent experience.
-
3+ years of professional experience in data science or applied machine learning.
-
Strong problem-solving and analytical skills, with the ability to turn complex product questions into actionable insights.
-
Excellent communication skills, both verbal and written, with the ability to present technical results to non-technical audiences.
-
Proven ability to build and maintain strong relationships with stakeholders across teams and functions.
-
Deep understanding of machine learning algorithms, from classical methods (e.g., regression, random forests, k-means) to advanced techniques (e.g., gradient boosting frameworks such as XGBoost, LightGBM, CatBoost, and transformer-based architectures like BERT or Sentence Transformers).
-
Proficiency in Python or R, and data manipulation tools/libraries such as Pandas and SQL.
-
Hands-on experience deploying models in production and managing ML lifecycle processes (monitoring, retraining, version control).
- Experience with cloud platforms (GCP, AWS, or Azure).
-
Familiarity with MLOps frameworks for deployment, monitoring, and automation.
-
Exposure to big data tools (e.g., Spark, BigQuery).
-
Understanding of A/B testing, experimentation frameworks, and causal inference techniques.
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.
