We are looking for a Machine Learning (ML) Data engineer who will partner with application teams and assist with data analysis and research; conducting tactical data extracts and build balanced ML and Data pipelines; deploy AI/ML models and build reports to measure deployed model efficiency.
Key Responsibilities:
- Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress
- Analyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability
- Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world
- Verifying data quality, and/or ensuring it via data cleaning
- Supervising the data acquisition process if more data is needed
- Finding available datasets online that could be used for training
- Defining validation strategies
- Defining the preprocessing or feature engineering to be done on a given dataset
- Defining data augmentation pipelines
- Training models and tuning their hyperparameters
- Analyzing the errors of the model and designing strategies to overcome them
- Deploying models to production
Requirements
- Hands-on experience in Data Warehouse, ETL, Data Modeling & Reporting.
- 7+ years of hands-on experience in productionizing and deploying Big Data platforms and applications, Hands-on experience working with: Relational/SQL, distributed columnar data stores/NoSQL databases, time-series databases, Spark streaming, Kafka, Hive, Redshift and more
- Familiarity with data pipelines and ML pipelines right from Data Extraction to Insights generation
- Highly skilled in SQL, Python, Spark, AWS S3, Hive Data Catalog, Parquet, Redshift, Airflow, and Tableau or similar tools.
- Proven experience in building a Custom Enterprise Data Warehouse or implementing tools like Data Catalogs, Spark, Tableau, Kubernetes, and Docker
- Deep knowledge of data structures and algorithms.
- Strong verbal and written communications skills are a must and work effectively across internal and external organizations and virtual teams.
- AWS Certified Data Engineer
Benefits
Exciting Projects: We focus on industries like High-Tech, communication, media, healthcare, retail and telecom. Our customer list is full of fantastic global brands and leaders who love what we build for them.
Collaborative Environment: You Can expand your skills by collaborating with a diverse team of highly talented people in an open, laidback environment — or even abroad in one of our global canters.
Work-Life Balance: Accellor prioritizes work-life balance, which is why we offer flexible work schedules, opportunities to work from home, and paid time off and holidays.
Professional Development: Our dedicated Learning & Development team regularly organizes Communication skills training, Stress Management program, professional certifications, and technical and soft skill trainings.
Excellent Benefits: We provide our employees with competitive salaries, family medical insurance, Personal Accident Insurance, Periodic health awareness program, extended maternity leave, annual performance bonuses, and referral bonuses.
•
Last updated on Aug 13, 2024