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DATE ADDED: Wed 02/10/2019

Sr. To Lead Data Scientist (Iot Startup, Up To $200K)

Sunnyvale, CA, US
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COMPANY: SKYROCKET VENTURES

JOB TYPE: Permanent, FullTime

Skyrocket Ventures is a recruiting firm for hundreds of high growth technology companies that range from industry leaders to top-tier startups. This opportunity is with one of our client companies for a full-time permanent hire. Please only apply if you are authorized to work in the U.S.

Please note that even if this job is not a perfect match, we encourage you to apply as long as it is in the ballpark. Companies are often flexible in hiring candidates who do not perfectly fit their written job description, as long as the most important qualifications are there and the candidate is good in general.

Most of the jobs we are recruiting for are not posted online, so if you would like to know of all the opportunities we have that match your interests and qualifications, then please get in touch with us.

Thank you!

Jeff Ruben
Regional Head of Talent
Skyrocket Ventures
Feel free to check out testimonials from some of our many candidates/clients here:

Sr. to Lead Data Scientist (IoT startup, up to $200k)
Sunnyvale, CA

The Internet of Things (IOT) space is projected to grow to a $1.7 trillion dollar industry by 2020. This IoT startup is innovating in the space, utilizing the latest technologies in a variety of areas such as artificial intelligence, computer science, distributed computing, stream & batch data services, large scale system design, and more. They already have large customers, and are rapidly growing.


The company is offering up to $200k in base salary, plus equity that could be lucrative.

Job Responsibilities:

- Executing data mining projects, training and deploying models over a typical duration of 2 to 12 months.
- Innovate, analyze customer requirements, develop solutions in the time box of the project plan, and execute and deploy the solutions.
- Integrate the data mining applications in the company's platform (on Docker or Android).

Qualifications:

- Experience analyzing, training and deploying at least three data mining models in the past. The models must have been validated as robust over at least an initial time period.
- 3 years of industry work experience developing data mining models which were deployed and used.
- Programming experience in Python, using data mining related libraries such as Scikit-Learn, NumPy, SciPy and Pandas.
- Data mining algorithm experience with prediction (neural nets, statistical regression, deep learning, decision trees, SVM, ensembles), clustering (k-means, DBSCAN or other), or Bayesian networks

Nice to have:

- Ability to set expectations, develops project plans and meets expectations.
- Experience adapting technical dialogue to the right level for the audience (i.e. executives) or specific jargon for a given vertical market and job function.
- An MS or Ph.D. in Computer Science, Math, Statistics or another engineering or technical discipline.
- Experience managing models in production over their full life cycle until model replacement was needed.
- Training or experience with Deep Learning technologies such as TensorFlow, Keras, convolutional neural networks (CNN) or Long Short Term Memory (LSTM) neural network architectures.
- Experience with shrinking deep learning models, optimizing to speed up execution time of scoring or inference.
- Experience with OpenCV or other image processing tools or libraries
- Experience with Cloud computing such as Google Cloud, Amazon AWS or Microsoft Azure.
- Experience with decision trees like XGBoost or Random Forests.
- Experience with Complex Event Processing (CEP) or other streaming data as a data source for data mining analysis
- Experience with time series algorithms from ARIMA to LSTM to Digital Signal Processing (DSP).
- Understanding of Bayesian Networks (BN), a.k.a. Bayesian Belief Networks (BBN) or Graphical Belief Networks (GBN)
- Vertical experience in Industrial Internet of Things (IoT) applications such as Energy (Oil and Gas, Wind Turbines), Manufacturing (Motors, chemical processes, tools, automotive), Smart Cities (Elevators, cameras on population or cars, power grid, or Transportation (Cars, truck fleets, trains)

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