Data Scientist Lead

Airport Development Group, Inc.

United States

2 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

3 years minimum

Working hours

Regular working hours

Job source

arc.dev

Tech stack

Airflow Data Analysis Big Data Cluster Analysis Collaborative Software Computer Programming Data Governance Data Visualization Github Python (Programming Language) Machine Learning Power BI

+14 more

Tensorflow Tableau (Software) Management of Software Versions Data Processing Feature Engineering Pytorch Model Validation Gitlab Git Scikit Learn Information Technology Xgboost Machine Learning Operations Software Library

Job description

Collaborate with product owners, data engineers, and partners across all product development stages - from concept and design to prototyping, testing, data curation, deployment, industry scaling, and end-of-life.
Collect, process, and analyse large data sets from various sources.
Design, build, and validate machine learning models and statistical algorithms.
Perform exploratory data analysis to uncover insights and trends.
Collaborate with stakeholders to define data requirements and objectives.
Communicate findings and recommendations to non-technical stakeholders.

Requirements

At least 10 years of experience in a proven role as a data scientist or similar function, with at least 3 years in managing a team of data scientists, machine learning engineers and big data specialists
Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, or a related field.
Strong knowledge of statistical methods and machine learning techniques such as regression, classification, clustering, time series forecasting, and anomaly detection.
Strong programming skills in Python (and/or R), with hands-on experience using machine learning libraries such as scikit-learn, XGBoost, TensorFlow, and PyTorch.
Solid understanding of data wrangling, exploratory data analysis, feature engineering, and model evaluation, paired with the ability to select or define appropriate success metrics grounded in real business context.
Familiarity with MLOps practices including model deployment, monitoring, and versioning of ML artifacts (e.g., MLflow, Airflow, integration with CI/CD pipelines).
Experience using Git for version control and familiarity with common collaboration tools such as GitHub or GitLab.
Experience with data visualization tools like Tableau or Power BI.
Excellent problem-solving skills and attention to detail.
Strong storytelling, communication and collaboration skills.
Awareness of data governance, privacy, and security practices (e.g., GDPR, PDPA).

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