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Data Scientist - Sunnyvale, United States - Blend
Description
Data Scientist
Hybrid in Northern California.
$70 - $100 per hour
12+ month contract
This opportunity is not eligible for sponsorship, or C2C arrangements.
This critical role focuses on leveraging state-of-the-art machine learning, deep learning, big data mining, and optimization techniques to tackle complex challenges in ads relevance, ranking, and campaign optimization. Joining Blend, you'll collaborate with top-tier machine learning scientists, engineers, and product managers to address some of the most intricate issues in e-commerce and advertising.
What You'll Do:
Develop sophisticated feature extraction algorithms for various advertising applications such as audience targeting, relevance and ranking, and performance optimization.
Design, deploy, and manage machine learning and statistical models to predict or estimate key signals for advertising product performance optimization.
Enhance ML Automation platforms to support the entire machine learning lifecycle, utilizing tools like MLflow, KubeFlow, and MetaFlow.
Construct, maintain, and oversee scalable data pipelines utilizing distributed computing frameworks (e.g., Hadoop/Spark) and job schedulers (e.g., Airflow/Azkaban).
What You'll Bring:
Proficiency in backend software development, system architecture, and object-oriented design.
Experience in high-level programming languages such as Python or Java, ensuring production code quality.
Familiarity with big data distributed computing frameworks, including Hadoop, MapReduce, and PySpark.
Skilled in machine learning modeling and data analysis tools like Pandas, SQL, Numpy, Scikit-learn, TensorFlow, PyTorch.
Proven ability to develop and implement machine learning models to address real-world problems, tailoring algorithms to specific applications.
Experience with NoSQL databases, such as Hive or HBase.
Equal Opportunity Employer:
Blend is an Equal Opportunity Employer. We thrive by embracing diversity and inclusivity, recognizing that our strength comes from understanding and valuing the unique backgrounds, experiences, and perspectives of our team members.