We are looking for an experienced data engineer to join our team. You will use various methods to transform raw data into useful data systems.
For example, you’ll create algorithms and conduct statistical analysis. Overall, you’ll strive for efficiency by aligning data systems with business goals.
To succeed in this data engineering position, you should have strong analytical skills and the ability to combine data from different sources.
Data engineer skills also include familiarity with several programming languages and knowledge of learning machine methods.
If you are detail-oriented, with excellent organizational skills and experience in this field, we’d like to hear from you.
Understanding business objectives and developing models that help to achieve them, along with metrics to track progress
Analyzing ML algorithms and ranking them by their success probability
Exploring and visualizing data
Identifying differences in data distribution that could affect performance
Verifying data quality, and / or ensuring it via data cleansing
Supervising the data acquisition process
Finding available datasets online
Defining data augmentation pipelines
Training models and tuning hyperparameters
Analyzing the errors of the model and designing strategies to overcome them
Set up and manage AI development and production infrastructure
Build data ingest and data transformation infrastructure
Build and convert AI / machine learning models into APIs so that other applications can access them
Test and deploy AI models into production
Help product managers and business stakeholders understand results of AI / ML models
Develop MVPs based on AI / machine learning
Use AI to empower the company with novel capabilities
Keep current of latest AI research relevant to our business domain.
Help AI product managers and business stakeholders understand the potential and limitations of AI when planning new products.
University degree (or equivalent) in quantitative field : Statistics, Mathematics, Computer Science, Electrical Engineering, Engineering Statistics, Systems Engineering, or relevant fields
Minimum of 3 years professional experience in Data Engineering
Experience in developing machine learning models and applying advanced analytics solutions to solve complex business problems
Proficiency with Python and basic libraries for machine learning such as scikit-learn and pandas
Experience with modern deep learning frameworks : RLlib, PyTorch, TensorFlow, etc.
Experience using statistical computer languages (R, Python, SLQ, Julia, MatLab etc.) to manipulate data and draw insights from large data sets.
Experience with distributed data / computing tools : Ray, Map / Reduce, Spark, etc.
Experience with NoSQL databases, such as MongoDB, Cassandra, HBase
Experience with unsupervised and supervised machine learning techniques and methods
Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.
and their real-world advantages / drawbacks.
Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.
and experience with applications
Experience working with and visualizing large-scale (e.g., terabyte and petabyte) unstructured and structured data sets and databases
Experience with the design or use of production pipelines for online learning and reinforcement learning
Experience working with and creating data architectures
Proven DevOps CI / CD, QA Automation experience
Experience performing good unit testing and peer reviews before delivering code to QA
Proficiency with SQL programming
Experience working with statistical software packages including : SAS, SPSS Modeler, R, WEKA, or equivalent
Excellent analytical and multitasking skills
Self-motivated and creative problem-solvers who love to challenge themselves
An ability to perform well in a fast-paced environment
Ability to select hardware to run an ML model with the required latency
Proficient understanding of code versioning tools, such as Bitbucket, Git, Mercurial, SVN etc.