Cloud Data Scientist / ML engineer (AWS, remote)

Published on Aug 30, 2021

ClearScale

A Product company with office in Kyiv, Ukraine.

Apply for a Job

This job is archived Explore similar jobs in Data Engineering

Experience:

1-3 years

Job Type:

Full-Time

Remote

Specializations:

Data Engineering

Deep Learning

Data Science & AI

What You Will Do Responsibilities

Select and justify the appropriate ML approach for a given business problem
Design and implement scalable, cost-optimized, reliable, and secure ML solutions
The ability to express the intuition behind basic ML algorithms
Create data repositories for machine learning
Identify and implement a data-ingestion solution
Identify and implement a data-transformation solution
Sanitize and prepare data for modeling
Perform feature engineering (missing and unbalanced data, outliers)
Analyze and visualize data for machine learning
Train machine learning models
Perform model tuning (learning rate, regularization techniques), hyperparameter optimization
Evaluate machine learning models
Deploy and operationalize machine learning solutions

What You Should Bring

Must-have

Bachelor or Specialist/Masters in Computer Science, Statistics, Informatics, Information Systems or another quantitative field
3+ years of experience in Machine Learning/Data Science applications (classical and deep learning models, ensemble learning)
3+ years of experience in Python ML frameworks (NumPy, SciPy, scikit_learn, Pandas, Jupyter, Matplotlib)

Nice-to-have

Knowledge of ANSI SQL (ability to write advanced analytical queries)
In-depth knowledge in one or more Machine Learning areas: Deep Learning, NLP, Recommender Systems, Reinforcement Learning
In-depth knowledge of Tensorflow/Keras
In-depth knowledge of AWS SageMaker and one or more of the following related algorithms: Linear Learner, XGBoost, Seq2Seq, DeepAR, BlazingText, Object2Vec, Object Detection, Image Classification, Semantic Segmentation, Random Cut Forest, Neural Topic Model, Latent Dirichlet Allocation, K-Nearest-Neighbors, K-Means, Principal Component Analysis, Factorization Machines, IP Insights, Reinforcement Learning, Automated Model Tuning
In-depth knowledge of one or more of the following AWS technologies: S3, Kinesis, Glue, Redshift, RDS, Aurora, DynamoDB, ElastiCache, Data Pipeline, Batch, DMS, Step Functions, Athena, QuickSight, EMR, SageMaker, Ground Truth, Comprehend, Translate, Transcribe, Polly, Rekognition, Forecast, Lex, Personalize, Textract, DeepRacer, DeepLens, IoT
Hands-on experience with Apache Spark MLLib (Zeppelin)
Hands-on experience with OpenCV
Hands-on experience with advanced Python data frameworks (Seaborn, PyTorch, Dask)