Senior Data Scientist - US Remote

Seek

4d•$145,000 - $175,000•Remote

About The Position

Seek, an Eldridge company, is on a mission to connect analytics developers and consumers like never before - driving business value in minutes, not months. Insight Cloud, the analytics app store for your business, was created to make it easy for everyone to build, distribute, and consume data & analytics apps in the thriving cloud analytics economy. Insight Cloud delivers instant access to curated, ready-to-use analytics apps from the leading providers in the market, accelerating your speed to insights by 15X+ while eliminating the pain of building, hosting, and maintaining an entire analytics ecosystem on your own. Built for developers and consumers alike, Insight Cloud is not just another tool to work with data - it is the platform connecting leading analytics developers with innovative consumers - driving business value faster than ever. Seek’s Data Science team is central to our mission making advanced predictive analytics and machine learning applications accessible to businesses of all sizes. Your work will involve exploring customer and partner data sets, developing predictive models, and applying machine learning techniques to uncover trends, patterns, and insights that can generate significant improvements to industry-wide practices. You will collaborate closely with stakeholders to build and launch new analytics apps, ensuring that the insights generated are accessible through Seek’s app store.

Requirements

5+ years of experience in data science, with a proven track record of deploying and training machine learning models
Advanced proficiency in Python ML libraries such as Scikit-learn, TensorFlow, and PyTorch for regression, classification, clustering, and deep learning techniques
Strong understanding of gradient boosting frameworks such as XGBoost and LightGBM for building high-performance models
Strong expertise in data and feature engineering including data cleaning, normalization, transformation, and creating/selecting relevant features from raw data
Strong expertise working with large-scale datasets directly within cloud data warehouses such as Snowflake, DataBricks, or Redshift
Advanced SQL skills: joins, subqueries, window functions, common table expressions (CTEs), aggregations, complex string manipulations, stored procedures and functions
Bachelor's degree in data science, statistics, computer science, or a related field.

Nice To Haves

Experience with SnowPark ML (Snowflake) for Python: DataFrame API, user-defined functions (UDFs), and ML Pipelines
ML performance optimization experience: efficient computation, parallel processing, hyperparameter turning, model pruning, and quantization
Experience with data visualization tools and techniques
Excellent problem-solving and creative thinking skills with ability to drive complex data analysis and development forward independently
Strong communication skills with the ability to present data and articulate complex technical concepts clearly and concisely

Responsibilities

Analyze large (and sparse) datasets to extract meaningful patterns, trends, and insights using advanced statistical methods and machine learning algorithms.
Construct and maintain data processing workflows, ensuring efficient mapping and transformation of client source data.
Perform data enrichment by sourcing additional datasets and integrating them with client data to enhance analytical depth and accuracy.
Develop complex SQL queries and Python scripts to transform and model data, tailoring configurations and fine-tuning parameters for optimal performance.
Translate business challenges into analytical frameworks, proposing and implementing data-driven solutions that have a tangible impact on our clients’ decision-making processes.
Create and maintain interactive dashboards that provide intuitive visualizations of data insights, enabling users to derive clear, actionable information.
Work closely with analytics engineers, other data scientists and business stakeholders to ensure that ML models are aligned with business goals and technical requirements.
Write clear and comprehensive documentation for ML models, pipelines, and workflows to facilitate collaboration and reproducibility.