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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and optimizing end-to-end recommendation systems, including building scalable ETL pipelines and deploying machine learning models on cloud platforms. Proficient in integrating multi-modal data and conducting A/B testing to enhance model performance and business outcomes.
Highest-signal resume keywords
Python ProgrammingMachine Learning Model DevelopmentETL Pipeline ConstructionCloud AI Platform DeploymentA/B Testing Design
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningDeep LearningSQLNoSQLData TransformationUnit TestingIntegration TestingCI/CD PipelinesDistributed TrainingBig Data Technologies
Tools & Technologies
PandasNumPyScikit-learnPyTorchTensorFlowAzure Data FactoryAWS GlueGoogle Cloud DataflowDockerSpark
Industry Keywords
Recommendation SystemsReal-Time ProcessingBatch ProcessingTransactional WorkloadsAnalytical WorkloadsMulti-Modal ModelsTransformer-Based ModelsPublications in Deep LearningModel VersioningExperiment Tracking
Tech Stack
Tools & technologiesAmazon RedshiftAWSAzureBigQueryCassandraCloudDockerETLGoogle Cloud PlatformHadoopKafkaMongoDBMySQLNoSQLNumpyPandasPostgresPythonPyTorchScikit-LearnSnowflakeSparkSQLTensorflow
About the role
Key responsibilities & impact- Design, implement, and optimize end-to-end recommendation pipelines from data ingestion to model inference
- Build and maintain scalable ETL pipelines for reliable and efficient data flows
- Develop, evaluate, and continuously improve machine learning models for recommendation systems
- Research, prototype, and implement state-of-the-art approaches to improve recommendation quality and business metrics
- Scale and optimize data and model pipelines for large-scale data and real-time or batch processing
- Integrate behavioral, transactional, contextual, and other multi-modal data into recommendation models
- Implement unit and integration tests across data, modeling, and deployment workflows
- Monitor and maintain data pipelines, model quality, system performance, and downstream impact
- Design and analyze A/B tests to evaluate model performance and support data-driven product decisions
- Build dashboards and observability tools for model metrics, system health, and business KPIs
- Collaborate with Data Engineers, Software Engineers, and stakeholders to deliver scalable, production-ready solutions
Requirements
What you’ll need- Bachelor's or Master's degree in Computer Science, Engineering, or a related field
- Strong Python experience with recent production use
- Hands-on experience with data science and machine learning libraries and frameworks, including Pandas, Polars, NumPy, scikit-learn, PyTorch, TensorFlow, JAX, or Hugging Face
- Experience building and deploying end-to-end machine learning systems on Azure, GCP, or AWS cloud AI platforms
- Experience with ETL pipelines, deployment and monitoring, model versioning, and experiment tracking
- Experience supporting batch or real-time machine learning workflows
- Strong understanding of deep learning–based recommender systems for next-item prediction
- Understanding of analogous NLP architectures that model sequential patterns and context
- Demonstrated experience building efficient data transformation pipelines for transactional (OLTP) and analytical (OLAP) workloads
- Strong knowledge of SQL and NoSQL databases, such as PostgreSQL, MySQL, Redshift, Snowflake, BigQuery, MongoDB, or Cassandra
- Experience with unit and integration testing, such as Pytest
- Experience with CI/CD pipelines and Docker-based containerization
- Experience building large-scale recommender systems is an advantage
- Publications in deep learning at relevant conferences or journals are an advantage
- Experience with Azure Data Factory, AWS Glue, or Google Cloud Dataflow is an advantage
- Experience designing and analyzing A/B tests and understanding relevant evaluation metrics is an advantage
- Experience designing and implementing metadata-driven pipelines to scale automated A/B testing systems is an advantage
- Experience developing multi-modal models integrating text, images, audio, or other data types is an advantage
- Experience applying transformer-based models or LLMs to recommendation or personalization tasks is an advantage
- Experience with distributed training, including data parallelism and model parallelism, is an advantage
- Experience with distributed data processing and big data technologies such as Spark, Hadoop, Flink, Kafka, Hive, Presto, or Databricks is an advantage