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Data Science Intern High Tech Jobs
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Details
What you’ll be doing:
- Bring the Data Science to punch in finding the best Models and features to capture the way fraudsters behave to improve real-time detection, in light of intelligent adversaries.
- Find techniques for automated learning from our data, which is meso-big and only sometimes tagged. In particular, lead the Machine Learning revolution in new fields where inference was so far heuristic.
- Communicate research results, solutions and complex ideas to peers, management, and customers. Be the Data Science face of the company.
- Measure and enable Info-gain-based introduction of new features into our system.
- Define and develop the way we should synthesize large corpus of analytical knowledge insights into a scalable streamed flow
- Develop measures, metrics and uses for our large graph of humans & ‘what they use on the internet’ entities.
- Working with colleagues from various disciplines - Research Engineers, Analysts, and your Data Scientists (all of whom write production code!)
- Proven record of at least 3 years of leading a team of experienced data scientists with clear KPIs and results that moved the needle.
- Proven ability to work well sideways (with peers and vendors) and upward (with management). Proven ability to communicate with customers; you are part of the sales cycle to the big enterprise customers.
- Proven ability to manage well. Your people are very effective, happy, energised, and grow.
- A PhD or M.Sc. in a quantitative field (e.g., Statistics, Economics, Sciences, Engineering, CS)
- Delivery oriented and capable: Algorithms, processes, specifications to R&D.
- Proactive. Tell us where we can use data more effectively, but we currently do not. Draw the boundaries of what possible - you are the expert.
- Knowledge of statistical methods, Bayesian probability, and machine learning techniques.
- Background in: Python, SQL, Spark
- Experience with Big Data, including parallel processing of NoSQL data stores - a plus.
- Can work independently given abstract tasks, understand and influence research for data.
- Professional proficiency in English.
- Proactive contribution to the data science community (Please add links to your Kaggle / Git account).
- Worked with not-so-big-data, skewed and noisy labeling, non-stationary world... but could still extract the best information possible.
- Open source projects you’ve created or contributed to.
- Interesting problems you’ve dealt with, that weren’t trivial.
- Side projects that you couldn’t resist building.
