Senior Data Scientist - US Remote

Role overview

Seek is hiring a Senior Data Scientist (US Remote) to help make advanced predictive analytics and machine learning applications accessible to businesses of all sizes. You will analyze customer and partner datasets, build predictive models, and help deliver insights through Seek’s analytics app store.

Responsibilities

• Analyze large (and sparse) datasets to find meaningful patterns, trends, and insights using statistical methods and machine learning.
• Build and maintain data processing workflows for mapping and transforming client source data.
• Enrich data by sourcing additional datasets and integrating them with client data.
• Develop complex SQL queries and Python scripts to transform and model data, tuning configurations for performance.
• Translate business problems into analytical frameworks and implement data-driven solutions.
• Create and maintain interactive dashboards for intuitive data visualizations.
• Collaborate with analytics engineers, data scientists, and business stakeholders to align ML models with goals and requirements.
• Write clear documentation for ML models, pipelines, and workflows to support reproducibility.

Requirements

• 5+ years of data science experience; proven track record deploying and training ML models.
• Advanced Python and ML libraries: scikit-learn, TensorFlow, PyTorch (regression, classification, clustering, deep learning).
• Strong gradient boosting experience: XGBoost and LightGBM.
• Strong data/feature engineering skills (cleaning, normalization, transformation, feature creation/selection).
• Experience working with large-scale datasets in cloud data warehouses: Snowflake, Databricks, or Redshift.
• Advanced SQL skills: joins, subqueries, window functions, CTEs, aggregations, complex string manipulation, stored procedures/functions.
• Bachelor’s degree in Data Science, Statistics, Computer Science, or related field.

Nice-to-haves

• Experience with SnowPark ML (Snowflake): DataFrame API, UDFs, and ML Pipelines.
• ML performance optimization: parallel processing, hyperparameter tuning, pruning, quantization.
• Experience with data visualization tools (truncated in source).