• Job Description

  Req#: 20116
  Why N-able
  
  

  At N-able, we’re not just helping businesses be secure —we’re redefining what it means to be cyber resilient. Our end-to-end platform blends AI-powered capabilities and flexible tech stacks, so customers can manage, secure, and recover with confidence. But the real power behind it all? Our people. We’re a global crew of N-ablites, who love solving complex problems, sharing knowledge, and delivering solutions that actually make a difference. If you're into meaningful work, fast growth, and a team that’s got your back, you’ll be surrounded by people who believe in what they do—and in you.

  If you are an affable and talented data practitioner with excellent attention to detail who enjoys a flexible, fast-paced work environment, our Data Science team is the place for you! We are a cloud-native, machine learning-empowered, one-stop cybersecurity solution that combines event monitoring, threat detection, and incident response. Our clients range from small regional financial institutions to multinational corporations.

  The Data Science team collaborates with a team of engineers to automate the ingestion of security logs into the cloud and builds machine learning solutions for empowering IT security. These applications have immediate impact on clients, allowing effective monitoring of billions of log messages and highly informative alerts on events that warrant further investigation. You will be joining a team of data scientists to provide critical support for building, enhancing, and maintaining the cloud infrastructure needed to train and deploy machine learning models.

  The Way We Work is our hybrid working model based on trust and flexibility.

  Please note that this role is open to candidates in North Carolina, Boston, DC, Maryland, and Virginia. Experience in Spark is required.

  
  What You'll Do
  
  

  • Maintain awareness of models in production and development, sharing with the team assessments of the related data infrastructure and solving cloud computing challenges in data ingestion, ETL and datalake creation, model training, and model inference
  • Solve technical challenges specific to keeping machine learning applications compatible with the cloud architecture in place while optimizing compute capacity and cost
  • Design and implement scalable methodologies that can handle streaming data for training and application of ML models in AWS
  • Utilize AWS services such as Kinesis, Firehose, Lambda, Batch, etc. as building blocks for the functions listed above and optimally interface those resources with Big Data platforms like Databricks
  • Write documentation on cloud infrastructure and systematically track performance
  • Develop and maintain testing environments and design procedures for change management
  • Partner with peers in the broader Engineering organization to streamline interactions between backend and frontend teams and reduce bottlenecks in the development/deployment process
  • Influence peers and leadership to advance promising solutions

  
  What You'll Bring
  
  

  • Bachelor’s Degree in STEM field – advanced degree a plus
  • 2-4 years’ experience in a machine learning developer assist role and general familiarity with data mining objectives and standard operating procedures
  • Non-STEM degree combined with long track record in data mining will be considered, especially candidates with exposure to cloud data solutions
  • AWS certifications preferred in Cloud Practitioner and/or Data Analytics
  • Experience in Spark and related Big Data frameworks
  • Experience building ETL pipelines in a cloud environment (AWS preferred)
  • Basic knowledge of machine learning techniques
  • Fluency in Python, SQL
  • Ability to perform data analysis to support data quality decisions
  • Convincing communicator and presenter
  • Willingness to learn new things and seek guidance from peers

  Bonus Qualifications

  • Experience working with high velocity data in streaming environments
  • Experience with Elasticsearch including complex queries and aggregations, python libraries and cluster management
  • Familiarity with Big Data warehousing platforms such as Databricks

  
  Purple Perks
  
  

  • Medical, dental and vision – for employee, partner, and children!
  • Generous PTO and observed holidays
  • 2 Paid VoluNteer Days per year
  • Pension Plan with company-contribution
  • Employee Stock Purchase Program
  • Discounted gym access at several local facilities
  • FuN-raising opportunities as part of our giving program
  • N-ablite Learning – custom learning experience as part of our investment in you
  • The Way We Work – our hybrid working model based on trust and flexibility

  
  About N-able
  
  

  At N-able, our mission is to protect businesses against evolving cyberthreats with an end-to-end cyber resilience platform to manage, secure, and recover. Our scalable technology infrastructure includes AI-powered capabilities, market-leading third-party integrations, and the flexibility to employ technologies of choice—to transform workflows and deliver critical security outcomes. Our partner-first approach combines our products with experts, training, and peer-led events that empower our customers to be secure, resilient, and successful.

  
  

  • Bachelor’s Degree in STEM field – advanced degree a plus
  • 2-4 years’ experience in a machine learning developer assist role and general familiarity with data mining objectives and standard operating procedures
  • Non-STEM degree combined with long track record in data mining will be considered, especially candidates with exposure to cloud data solutions
  • AWS certifications preferred in Cloud Practitioner and/or Data Analytics
  • Experience in Spark and related Big Data frameworks
  • Experience building ETL pipelines in a cloud environment (AWS preferred)
  • Basic knowledge of machine learning techniques
  • Fluency in Python, SQL
  • Ability to perform data analysis to support data quality decisions
  • Convincing communicator and presenter
  • Willingness to learn new things and seek guidance from peers

  Bonus Qualifications

  • Experience working with high velocity data in streaming environments
  • Experience with Elasticsearch including complex queries and aggregations, python libraries and cluster management
  • Familiarity with Big Data warehousing platforms such as Databricks

  
  

  • Maintain awareness of models in production and development, sharing with the team assessments of the related data infrastructure and solving cloud computing challenges in data ingestion, ETL and datalake creation, model training, and model inference
  • Solve technical challenges specific to keeping machine learning applications compatible with the cloud architecture in place while optimizing compute capacity and cost
  • Design and implement scalable methodologies that can handle streaming data for training and application of ML models in AWS
  • Utilize AWS services such as Kinesis, Firehose, Lambda, Batch, etc. as building blocks for the functions listed above and optimally interface those resources with Big Data platforms like Databricks
  • Write documentation on cloud infrastructure and systematically track performance
  • Develop and maintain testing environments and design procedures for change management
  • Partner with peers in the broader Engineering organization to streamline interactions between backend and frontend teams and reduce bottlenecks in the development/deployment process
  • Influence peers and leadership to advance promising solutions

• About the company

  Software, resources, and tools for MSPs and IT departments with best-in-class Remote Monitoring & Management, Data Protection, and Security solutions.

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