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Data Scientist

Team: Machine Learning and Artificial Intelligence
Location: India

Purpose of the role

To be able to prepare data sets for machine learning, and where appropriate, initiate the creation of machine learning models and data pipelines. Understand and visualise the data being presented and work with Subject Matter Experts to validate the quality of the findings.

Who you will be working with

Technology Manager: Head of AI, Components and Asset Management
– Working together on a day-to-day basis
Innovation Team: People from various areas of the business
– Presenting experiment results, gathering feedback on ideas, helping with the interpretation of data and outcomes
– Working with developers, as required, to obtain access to data and identify potential solutions or experiments to solve customer challenges
Industry Partners:
– Liaising with external flydocs partners on the continuous development and improvement of processes
India and globally-based colleagues:
– Collaborating with flydocs employees from across the world at social events, regional activities, projects, and other initiatives

How do you get to contribute to the business?

• Identifying areas to increase efficiency of transactions and process automation
• Mining and analysing Big Data, generate a hypothesis, draw valid inferences, and present them to management
• Owning statistical analysis and modeling, and advanced quant projects end to end, including delivering insights to stakeholders
• Owning the behavioural data collection strategy, identifying the most relevant bits of information for the business
• Setting up and maintaining automated data processes
• Developing and supporting the reporting process
• Advising on and delivering suitable analytics solutions to address business opportunities
• Using your strong knowledge of statistical topics, e.g. linear regression, hypothesis testing, sample selection
• Participating and contributing to Data Science product enhancements

Growth opportunities

We will look to develop your skills as a Data Scientist within the flydocs team and also look to expand your skills by working with our key partners and customers. You will have the opportunity to lead discussions with key stakeholders and introduce new technologies and ideas.

You can look forward to partnering with the following collaborators to create value for our customers:

  • Azure cognitive services
  • Machine Learning specialist companies
  • Developers and User Experience Experts
  • Aviation Subject Matter Experts
  • Customer-facing colleagues, CSM, Sales and Marketing
  • Business Intelligence Teams

This is how we will support your onboarding journey to enable you to start adding value almost immediately

In your first 30 days you will:
• Get to know your key colleagues and build great working relationships with them
• Set up the data pipeline plan for a project or two
• Set up a few experiments with Machine Learning tools

By 60 days you will have:
• Delivered a production-ready data pipeline
• Defined the processes and pipelines that flydocs will be using

By 90 days you will:
• Be a key member of the Data Science team
• Have designed and prepared a product improvement from idea initiation to materialisation

We would be really happy if you had

• 3 years Data Science experience
• Machine Learning experience
• Ability to work independently
• Excellent numerical and analytical skills
• Experience in statistical methodologies and data analysis techniques
• Comfortable working with structured and non-structured databases
• Solid hands-on skills in sourcing, cleaning, manipulating, analysing, visualising, and modeling real data
• Any experience of BI software would be advantageous – for example, Tableau, Power BI, etc.
• Technical proficiency in Python and SQL
• Experience in Auto ML platforms (AWS Sage Maker, Google Auto ML, Datarobot) or equivalent
• Ability to produce clear data visualisations and graphical representation

Other important bits:
• Python, visual studio, command line
• Google Cloud Platform
• Azure Portal, including Containers, Blobs, Keys and SAS tokens
• Azure Data Factory, Data Lakes and Machine Learning Studio
• Understanding of Human in the loop
• Google Data Flow
• Excel, Power BI
• Aviation data analysis

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