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Andriy L., Data Science / Java Software Engineer

Ternopil, Ukraine
Last Updated: 4 Jul 2023
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• 10+ year experience with JAVA and Linux operating systems: Java 11, Spring Boot, Spring Data JPA, MySQL/MongoDB, ElasticSearch, Jenkins CI • Big Data, Data Engineering, NLP, Corpus Linguistics, ML, DL • Design and implement a distributed warehouse system (AWS migration), REST API design and cache implementation (1,000-10,000 requests/sec), Design and implement high-load microservices • Deliver special courses devoted to concurrency and prepared handbook on “Parallel and Distributed Computations” [technologies: (basics of OpenMP, MPI, CUDA, OpenCL), advanced in Java concurrency]; • Scientific projects at university related research increasing effectiveness of crypto-analysis • Upper-Intermediate English • Available Full-time • Ready to start in 2 weeks • No scheduled vacations within next 3months

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Apache Spark ML

Apache Spark ML

Java

Java   15 yr.

Hadoop ecosystem

Hadoop ecosystem

Python

Python   3 yr.

View Andriy

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Bring new talent on board with a trial period to confirm you hire the right one. There are no termination fees or hidden costs.

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FAQs about Apache Spark ML Development

How do I hire a Apache Spark ML developer? Arrow

Are you desperately looking for a Apache Spark ML developer validated to your requirements and skills? Are you lacking in resources and tools to find the right developer? If yes, then you are at the right place! Because at Upstaff, the selection of the Apache Spark ML Developer is done professionally as per your requirement, in a short time of just a few days, from calling to the instant task of your validated developer, with all your required skills.

Where is the best place to find Apache Spark ML developers? Arrow

Sure enough, within the network there must be dozens, if not hundreds, of services and platforms where you can look for the Apache Spark ML guy you need, but just Upstaff lets you sort out the best of the best real qualified developers practically in a couple of minutes, so that you will be able to select the one whose personality will be a perfect fit to your company. This is the case when you can create a genuinely “Pizza” type of service in the field of custom software development.

How are Upstaff Apache Spark ML developers different? Arrow

Vetting by expert human reviewers is combined with historically curated feedback from clients and team-mates, which has enabled us to save, on average, more than 50 hours for each client team evaluating Apache Spark ML candidates for every position. We live and breathe technical excellence, borne out of extensive industry expertise.

How quickly can I hire Apache Spark ML developers through Upstaff? Arrow

Our process begins with a 30-minute discovery call: we’ll discuss your pain points, technical requirements, and team diversity. Meet Carefully Matched Apache Spark ML Talents. Upon your meeting confirmation, we’ll send you profiles and arrange an introduction call with the right talents for your project, within 1-3 days. Schedule a call to meet Apache Spark ML engineers in person. Make the right call. Bring a new Apache Spark ML developer on board with a trial period: confirm that the person you hired does the job right, no termination fees and no hidden fees.

How does Upstaff vet remote Apache Spark ML engineers? Arrow

After this introductory round, Upstaff Managers do an eval of the soft skills of the candidates before the hiring decision is approved or rejected. For the hard skills, the Upstaff Staffing Platform has a short testing session to check their Apache Spark ML skills or engage a senior developer to do a technical interview for more senior developers. You can reuse some of the hard and soft skills of past candidates to connect them with other jobs in the future (see below). Upstaff managers also do an eval of the talent before approving them for further work, allowing us to accelerate the work and ability to scale, but also providing information to clients on the talent pipeline. Finally, we also have a matching engine within the work mining system that matches candidates and jobs in real time.

How to hire Apache Spark ML developer? Arrow

If recruiting services and job boards are not your 1st option, you may want to consider a client-oriented service like Upstaff to find & hire Apache Spark ML developers. Upstaff has a network of vetted and tested Apache Spark ML developers. Here, you can hire web developers for a few simple coding tasks with Apache Spark ML.

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Table of Contents

How and where is Apache Spark ML used?

  • Customer Segmentation: Identify customer segments based on behavior
  • Fraud Detection: Detect fraudulent activities in real-time
  • Sentiment Analysis: Analyze sentiment of customer reviews
  • Recommendation Engine: Provide personalized recommendations
  • Image Recognition: Identify objects in images
  • Anomaly Detection: Detect unusual patterns in data
  • Churn Prediction: Predict customer churn likelihood
  • Clickstream Analysis: Analyze user behavior on websites
  • Predictive Maintenance: Predict when equipment needs maintenance
  • Healthcare Analytics: Analyze patient data for insights

Compare Junior, Middle, Senior, and Expert/Team Lead Apache Spark ML Developer roles

Seniority NameYears of experienceResponsibilities and ActivitiesAverage salary (USD/year)
Junior0-2 years
  • Assist in developing basic ML models
  • Participate in data preprocessing
  • Debug code
$60,000
Middle3-5 years
  • Develop ML models independently
  • Optimize algorithms
  • Train junior developers
$80,000
Senior6-8 years
  • Lead ML projects
  • Design complex algorithms
  • Provide technical guidance
$100,000
Expert/Team Lead9+ years
  • Set ML strategy
  • Manage ML team
  • Ensure project success
$120,000

Quick Facts about Apache Spark ML.

  • Apache Spark ML was created in 2014 by Matei Zaharia.
  • It is widely used in projects involving machine learning algorithms.
  • The entry threshold for Apache Spark ML is relatively low.
  • Apache Hadoop is one of the most popular technologies related to it.
  • Fun Fact: Apache Spark ML can process data up to 100 times faster than Hadoop.

TOP Apache Spark ML Related Technologies

  1. Spark NLP
  2. (John Snow Labs, 2017)

  3. TensorFlow
  4. (Google Brain Team, 2015)

  5. Keras
  6. (François Chollet, 2015)

  7. PyTorch
  8. (Facebook AI Research lab, 2016)

What are top Apache Spark ML instruments and tools?

  • MLlib: Apache’s ML library
  • SparkNLP: John Snow Labs, 2019
  • MLflow: Databricks, 2018
  • Spark-TSNE: Liang-Wei, 2015
  • BigDL: Intel, 2016
  • H2O Sparkling Water: H2O.ai, 2017

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