Shyftlabs6mo ago

Data Scientist

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Data ScienceData ScientistDataData & AI

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Quick Summary

Overview

About ShyftLabs ShyftLabs is a fast-growing data product company founded in early 2020, working primarily with Fortune 500 clients.

Technical Tools

Data ScienceData ScientistDataData & AI

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.