Senior Data Scientist - Fluent English

artefactlinkedin; United States; Remote; USD 140,000 - 190,000; Full time

Benefits offered by this job

Remote‑first setup

Meal and transportation benefits

Gympass and insurance

Job summary

artefactlinkedin is seeking a senior data scientist to design, build, and maintain scalable data products that enable analytics and informed decision making across the business.

Role blends hands‑on ML engineering with technical leadership in a remote‑first international team, using fluent English. You’ll mentor juniors, ensure data quality, and deliver production‑grade models with monitoring.

Qualifications

• Degree in Computer Science, Statistics, Engineering, Economics, or another quantitative field.
• Strong Python skills for data science, including pandas, NumPy, scikit‑learn, and PySpark.
• Hands‑on experience with cloud platforms (GCP, AWS, Azure, Databricks).
• Familiarity with data warehousing and databases (PostgreSQL, SQL Server, BigQuery, Snowflake).
• Experience with supervised models (XGBoost, logistic regression) and clustering.
• Working knowledge of MLOps tooling (Databricks, MLflow, SageMaker, Vertex AI) and Git workflows (GitHub, Azure DevOps, GitLab).
• Strong communication, mentoring ability, problem solving, and fluent English for an international environment.

Responsibilities

• Design, develop, and deploy end‑to‑end ML models with data acquisition and feature engineering.
• Collaborate with analysts, product managers, and engineers to translate problems into DS frameworks.
• Engineer features from large datasets to boost model accuracy and business performance.
• Ensure data quality and define dataset requirements for analytics and modeling.
• Monitor deployed models, handle drift and performance bottlenecks.
• Document methodologies, findings, and strategic recommendations for technical and non‑technical audiences.
• Mentor junior data scientists and analysts through code reviews and design sessions.

Skills

Python

Pandas

NumPy

scikit-learn

PySpark

GCP

AWS

Azure

Databricks

PostgreSQL

SQL Server

BigQuery

Snowflake

XGBoost

Logistic regression

Clustering

MLOps

Databricks MLflow

SageMaker

Vertex AI

GitHub

Azure DevOps

GitLab

Communication

Education

Degree in Computer Science or quantitative field

Tools

Databricks

MLflow

SageMaker

Vertex AI

GitHub

Azure DevOps

GitLab

BigQuery

Snowflake

Job description

Role overview

A senior data scientist role responsible for designing, building, and maintaining scalable data products that support analytics and decision-making across the business. The role combines hands‑on machine learning engineering with technical leadership and mentoring, and is delivered through a remote‑first, international team that operates in fluent English.

Responsibilities

• Design, develop, and deploy end‑to‑end machine learning models, covering data acquisition, feature engineering, and ongoing production monitoring.
• Collaborate with analysts, product managers, and engineers to translate business problems into data science frameworks with measurable outcomes.
• Engineer and refine complex features from large datasets to lift model accuracy and business performance.
• Champion data quality and integrity, defining dataset requirements suited for advanced analytics and modeling.
• Monitor, analyze, and troubleshoot deployed models, handling concept drift, quality degradation, and performance bottlenecks.
• Document and present methodologies, model findings, and strategic recommendations to both technical and non-technical audiences.
• Mentor junior data scientists and analysts through code reviews, design sessions, and knowledge sharing.

Requirements

• Degree in Computer Science, Statistics, Engineering, Economics, or another quantitative field.
• Strong Python skills for data science, including pandas, NumPy, scikit‑learn, and PySpark.
• Hands‑on experience with at least one major cloud platform such as GCP, AWS, Azure, or Databricks.
• Familiarity with data warehousing and databases such as PostgreSQL, SQL Server, BigQuery, or Snowflake.
• Practical experience with supervised models (e.g., XGBoost, logistic regression) and unsupervised approaches like clustering.
• Working knowledge of MLOps tooling such as Databricks, MLflow, SageMaker, or Vertex AI, plus Git‑based workflows on GitHub, Azure DevOps, or GitLab.
• Strong communication, mentoring ability, problem‑solving skills, curiosity, collaboration, and an entrepreneurial mindset, with fluent English for an international environment.

Nice to have

Experience with a deep learning framework such as PyTorch or TensorFlow/Keras.

Benefits and work setup

• Remote‑first Free Office arrangement.
• Meal and transportation benefits (VR/VT).
• Gympass, plus life, health, and dental insurance.
• Woba coworking credits usable anywhere.
• Three in‑person "Get Together" gatherings per year with workshops, talks, training, and social time.
• Semiannual reviews offering pathways for promotion.