11 of 11 Remote Apache Beam Jobs

for data analysis purposes. Experience with common DBMS (PostgreSQL, MySQL, MongoDB etc.). Experience with data warehousing and parquet data manipulation (e.g. Athena, Redshift, BigQuery). Experience with Spark, Beam, Kafka, Hadoop or other data processing tools. Fluency in Python and experience on production-quality software development. Solid software engineering principles (software design patterns, configuration management, source control, build More ❯

make personalization smarter, faster, and more impactful. OUTCOMES Build and deploy ML models serving 100M+ predictions per day to personalize user experiences at scale Enhance data processing pipelines (Spark, Beam, Dask) with efficiency and reliability improvements Design ranking algorithms that balance relevance, diversity, and revenue Deliver real-time personalization with latency Run statistically rigorous A/B tests to More ❯

make personalization smarter, faster, and more impactful. OUTCOMES Build and deploy ML models serving 100M+ predictions per day to personalize user experiences at scale Enhance data processing pipelines (Spark, Beam, Dask) with efficiency and reliability improvements Design ranking algorithms that balance relevance, diversity, and revenue Deliver real-time personalization with latency Run statistically rigorous A/B tests to More ❯

make personalization smarter, faster, and more impactful. OUTCOMES Build and deploy ML models serving 100M+ predictions per day to personalize user experiences at scale Enhance data processing pipelines (Spark, Beam, Dask) with efficiency and reliability improvements Design ranking algorithms that balance relevance, diversity, and revenue Deliver real-time personalization with latency Run statistically rigorous A/B tests to More ❯

make personalization smarter, faster, and more impactful. OUTCOMES Build and deploy ML models serving 100M+ predictions per day to personalize user experiences at scale Enhance data processing pipelines (Spark, Beam, Dask) with efficiency and reliability improvements Design ranking algorithms that balance relevance, diversity, and revenue Deliver real-time personalization with latency Run statistically rigorous A/B tests to More ❯

make personalization smarter, faster, and more impactful. OUTCOMES Build and deploy ML models serving 100M+ predictions per day to personalize user experiences at scale Enhance data processing pipelines (Spark, Beam, Dask) with efficiency and reliability improvements Design ranking algorithms that balance relevance, diversity, and revenue Deliver real-time personalization with latency Run statistically rigorous A/B tests to More ❯

make personalization smarter, faster, and more impactful. OUTCOMES Build and deploy ML models serving 100M+ predictions per day to personalize user experiences at scale Enhance data processing pipelines (Spark, Beam, Dask) with efficiency and reliability improvements Design ranking algorithms that balance relevance, diversity, and revenue Deliver real-time personalization with latency Run statistically rigorous A/B tests to More ❯

make personalization smarter, faster, and more impactful. OUTCOMES Build and deploy ML models serving 100M+ predictions per day to personalize user experiences at scale Enhance data processing pipelines (Spark, Beam, Dask) with efficiency and reliability improvements Design ranking algorithms that balance relevance, diversity, and revenue Deliver real-time personalization with latency Run statistically rigorous A/B tests to More ❯

make personalization smarter, faster, and more impactful. OUTCOMES Build and deploy ML models serving 100M+ predictions per day to personalize user experiences at scale Enhance data processing pipelines (Spark, Beam, Dask) with efficiency and reliability improvements Design ranking algorithms that balance relevance, diversity, and revenue Deliver real-time personalization with latency Run statistically rigorous A/B tests to More ❯

make personalization smarter, faster, and more impactful. OUTCOMES Build and deploy ML models serving 100M+ predictions per day to personalize user experiences at scale Enhance data processing pipelines (Spark, Beam, Dask) with efficiency and reliability improvements Design ranking algorithms that balance relevance, diversity, and revenue Deliver real-time personalization with latency Run statistically rigorous A/B tests to More ❯

make personalization smarter, faster, and more impactful. OUTCOMES Build and deploy ML models serving 100M+ predictions per day to personalize user experiences at scale Enhance data processing pipelines (Spark, Beam, Dask) with efficiency and reliability improvements Design ranking algorithms that balance relevance, diversity, and revenue Deliver real-time personalization with latency Run statistically rigorous A/B tests to More ❯