Data Engineer - Prunedge
5d ago
source : BetaJob

Job Brief

  • 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.
  • Responsibilities

  • 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.
  • Requirements

  • 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.
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