Applied Scientist - Remote

| San Francisco
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Harness is a rapidly growing startup that is disrupting the software delivery market. We are building an intelligent software delivery platform that enables engineers to deliver software faster, with higher quality, and less effort. The Harness Software Delivery Platform includes Continuous Integration, Continuous Delivery, Continuous Efficiency, Continuous Verification, and Continuous Features. The platform is designed to help companies accelerate their cloud initiatives as well as their adoption of containers and orchestration tools like Kubernetes and Amazon ECS.

Harness has raised $115 million in financing, reaching a valuation of $1.7 billion in just three years after launching from stealth. Alkeon Capital led a $85 million Series C round, along with new investors Battery Ventures, Citi Ventures, Norwest Venture Partners, Sorenson Capital, and Thomvest Ventures. The total funding includes a $30 million Series B-1 funding round, led by existing investors Menlo Ventures, IVP, and Unusual Ventures. 

The mission of the Continuous Verification team is to bring AI to the field of Software Delivery. Our fortune 500 customers rely on our strong AI to make changes to their software (via deployments) and infrastructure (either on-premise or on the cloud) with confidence. We strive to reduce mean time to detect (MTTD) using powerful anomaly detection, correlations and predictive analytics, and reduce mean time to recover (MTTR) by bringing visibility into the root cause. We stream thousands of planned and unplanned activities, and hundreds of thousands of telemetry signals & log data every minute to pinpoint regressions in performance, errors and business metrics and correlate that with the thousands of activities going on. 

POSITION SUMMARY

We are looking for experienced Applied Scientists with a deep passion for building machine-learning solutions to solve real world problems. You will closely work with engineering, product management, and customer support functions to understand the problem domain, create strategies to source data and develop insights, propose and validate machine learning based solutions, gather metrics and access real customer impact. 

KEY RESPONSIBILITIES

  • Build unsupervised machine learning models to deliver scalable solutions to business problems.
  • Run A/B experiments, gather data, and perform statistical analysis.
  • Build scalable pipelines and solutions for data analysis, supervised machine-learning models, model validation and serving.
  • Work closely with software engineers on detailed requirements, technical designs and implementation of end-to-end solutions in production
  • Research new machine learning approaches.
  •  #LI-Remote

ABOUT YOU

  • Master degree or Phd in computer science or related field with at least 2 years of experience 
  • Experience in building scalable machine-learning models for anomaly detection in time series and log data
  • Experience with clustering techniques, probabilistic models, auto encoders, few shot learning, sentiment analysis, correlation & predictive modeling, nlp techniques
  • Experience with python scikit learn, tensorflow
  • Strong data structures, algorithm design, and problem solving skills
  • Self starter, strong team player, delivery oriented

WHAT YOU WILL HAVE AT HARNESS

  • Competitive salary and early-stage stock options
  • Comprehensive healthcare benefits
  • Flexible Spending Account (FSA)
  • Flexible work schedule
  • Employee Assistance Program (EAP)
  • Paid Time Off and Parental Leave
  • Monthly, quarterly, and annual social and team building events
  • TGIF-Off program
  • Remote office stipend
  • Monthly internet reimbursement
  • Monthly Food & Beverage Reimbursement Program

HARNESS IN THE NEWS

  • Harness snags $85M Series C on $1.7B valuation as revenue grows 3x
  • #6 - Glassdoor Best Places to Work 2021 list
  • #17 on Forbes Top 50 Cloud Companies to Work For
  • #47 on LinkedIn’ Top 50 Companies to Work For

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, or national origin.

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Location

We are located in the SoMa district of San Francisco. Situated near all major transport hubs with plenty of local bars and restaurants nearby.

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