AVP Analytics

What you need to have:

Job Description

We are looking for an experienced engineer to join our data science team, who will help us design, develop, and deploy machine learning models in production. You will develop robust models, prepare their deployment into production in a controlled manner, while providing appropriate means to monitor their performance and stability after deployment.

What You’ll Do will include (But not limited to):

• Preparing datasets needed to train and validate our machine learning models
• Anticipate and build solutions for problems that interrupt availability, performance, and stability in our systems, services, and products at scale.
• Defining and implementing metrics to evaluate the performance of the models, both for computing performance (such as CPU & memory usage) and for ML performance (such as precision, recall, and F1)
• Supporting the deployment of machine learning models on our infrastructure, including containerization, instrumentation, and versioning
• Supporting the whole lifecycle of our machine learning models, including gathering data for retraining, A/B testing, and redeployments
• Developing, testing, and evaluating tools for machine learning models deployment, monitoring, retraining.
• Working closely within a distributed team to analyze and apply innovative solutions over billions of documents
• Supporting solutions ranging from rule-bases, classical ML techniques  to the latest deep learning systems.
• Partnering with cross-functional team members to bring large scale data engineering solutions to production
• Communicating your approach and results to a wider audience through presentations

Your Qualifications:

• Demonstrated success with machine learning in a SaaS or Cloud environment, with hands–on knowledge of model creation and deployments in production at scale
• Good knowledge of traditional machine learning methods and neural networks
• Experience with practical machine learning modeling, especially on time-series forecasting, analysis, and causal inference.
• Experience with data mining algorithms and statistical modeling techniques for anomaly detection in time series such as clustering, classification, ARIMA, and decision trees is preferred.
• Ability to implement data import, cleansing and transformation functions at scale
• Fluency in Docker, Kubernetes
• Working knowledge of relational and dimensional data models with appropriate visualization techniques such as PCA.
• Solid English skills to effectively communicate with other team members

Due to the nature of the role, it would be nice if you have also:

• Experience with large datasets and distributed computing, especially with the Google Cloud Platform
• Fluency in at least one deep learning framework: PyTorch, TensorFlow / Keras
• Experience with No–SQL and Graph databases
• Experience working in a Colab, Jupyter, or Python notebook environment
• Some experience with monitoring, analysis, and alerting tools like New Relic, Prometheus, and the ELK stack
• Knowledge of Java, Scala or Go-Lang programming languages
• Familiarity with KubeFlow
• Experience with transformers, for example the Hugging Face libraries
• Experience with OpenCV

About Egnyte

In a content critical age, Egnyte fuels business growth by enabling content-rich business processes, while also providing organizations with visibility and control over their content assets. Egnyte’s cloud-native content services platform leverages the industry’s leading content intelligence engine to deliver a simple, secure, and vendor-neutral foundation for managing enterprise content across business applications and storage repositories. More than 16,000 customers trust Egnyte to enhance employee productivity, automate data management, and reduce file-sharing cost and complexity. Investors include Google Ventures, Kleiner Perkins, Caufield & Byers, and Goldman Sachs. For more information, visit www.egnyte.com

#LI-Remote

• 6+ months of proven experience as a Data Scientist or Data Analyst
• Understanding of machine-learning and operations research
• Extensive knowledge of R, SQL and Excel
• Analytical mind and business acumen
• Strong Statistical understanding
• Problem-solving aptitude
• BSc/BA in Computer Science, Engineering or relevant field; graduate degree in Data Science or other quantitative field is preferred

Data analyst responsibilities include conducting full lifecycle analysis to include requirements, activities and design. Data analysts will develop analysis and reporting capabilities. They will also monitor performance and quality control plans to identify improvements.

• Interpret data, analyze results using statistical techniques and provide ongoing reports
• Develop and implement databases, data collection systems, data analytics and other strategies that optimize statistical efficiency and quality
• Acquire data from primary or secondary data sources and maintain databases/data systems
• Identify, analyze, and interpret trends or patterns in complex data sets
• Filter and “clean” data by reviewing computer reports, printouts, and performance indicators to locate and correct code problems
• Work with management to prioritize business and information needs
• Locate and define new process improvement opportunities
  
  

  Requirements

  • Proven working experience as a Data Analyst or Business Data Analyst
  • Technical expertise regarding data models, database design development, data mining and segmentation techniques
  • Strong knowledge of and experience with reporting packages (Business Objects etc), databases (SQL etc), programming (XML, Javascript, or ETL frameworks)
  • Knowledge of statistics and experience using statistical packages for analyzing datasets (Excel, SPSS, SAS etc)
  • Strong analytical skills with the ability to collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy.
  • Adept at queries, report writing and presenting findings
  • BS in Mathematics, Economics, Computer Science, Information Management or Statistics