Senior Machine Learning Engineer

San Francisco, California
US$165000 - US$185000 per year + Competitive Benefits

This vacancy has now expired. Please see similar roles below...

Senior Machine Learning Engineer - Media

San Francisco Bay Area

$165,000 - $185,000 + Competitive Benefits

The Company

Harnham is working with a revolutionary media streaming platform. They host content from hundreds of partners and make thousands of movie and TV titles accessible across many supported devices. A lot of investors are jumping in - it's your chance to really push the organization with your machine learning expertise!

If you love to work with streaming data, building recommendation engines, and using NLP to personalize an experience - then this is the role for you! This late-stage venture company employs thousands of people around the world and you will be leading the team that makes this platform so successful.

In this role, managing a team of machine learning engineers as both a leader and individual contributor. This opportunity will have you taking ownership and given recognition for the platform and business tools you will help build. You're going to work with your core team in the Bay, an off-shore team, and C-level management. Most of all, this company has an amazing office in an invigorating neighborhood and food is always available in the office!

The Role

You will:

  • Manage a team of data scientists and machine learning engineers (some off-shores) to train models, develop machine learning product solutions, and business tools
  • Focus heavily on sentiment and predictive analytics to guide the user's feed and behavior with media recommendations
  • Develop business and platform tools and take to production to improve the workplace efficiency and streaming services
  • Play a critical role in structuring the team's direction and scalability, from the challenges you will work on to the hiring process of your team

Skills and Expertise

You have:

  • Developed and optimized search and service recommendation engines using natural language processing and behavioral analytics to cater a personalized experience to consumers and service providers
  • Expertise in Python and/or Scala, implementing machine learning techniques with python libraries, like TensorFlow, Scikit-learn, Pandas, and Keras
  • HUGE PLUS! - Exposure to Spark technologies at an industry level
  • Exceptional communication skills - you can deliver information to just about anyone to drive business decisions and develop your team's direction

Benefits

$165,000 - $185,000 + Competitive Benefits

How to Apply

Please register your interest by sending your CV to Karla Guerra at Harnham via the Apply link on this page.

For more information this role or other Data Science opportunities, please contact Karla Guerra at Harnham.


KEYWORDS
Python, R, Machine Learning, Deep Learning, Big Data, AI, Artificial Intelligence, Java, C++, Spark, AWS, Scala, Modelling, Algorithm, Streaming, Content, Media, Video, Content Analysis, Sentiment Analysis, Data Scientist, Data Science, Scikit-learn, TensorFlow, PyTorch, Keras, Pandas, Classification, Clustering, Extraction, Recommendation Engine, Recommendation Systems, Search Recommendation, Recommendation Engine, Reinforced, Supervised, Unsupervised, Learning

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54093
San Francisco, California
US$165000 - US$185000 per year + Competitive Benefits

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Harnham blog & news

With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.

Visit our Blogs & News portal or check out our recent posts below.

A Data Engineer is a Unique Blend of Data Professional

From startup and small business to large enterprises, each type of business requires a unique blend of Data professional. Though in today’s world, much of the Data being gathered, catalogued, and analyzed happens both in the Cloud and on a hard drive, each type of business has a different need, budget, goals, and objectives. But there is one thing each and every business will have in common. At the heart of the Data team will be a Data Engineer. The Three Main Roles of a Data Engineer This is an analytics role in high demand. It is a growing and lucrative field with steps and stages for nearly every level of business and education experience. For example, a Data Scientist interested in stepping into a Data Engineer role might begin as a Generalist. In all, there are three main roles for each level and type of business – Generalist, Pipeline-Centric, and Data-Centric. Let’s take a quick look at each of the roles with an eye toward the type of person who might be the best fit: Generalist – Most often found on a small team, this type of Data Engineer is most likely the only Data-focused person in the company. They may have to do everything from build the system to analyze it, and while it carries its own unique set of skills, it doesn’t require heavy architecture knowledge as smaller companies may not yet be focusing on scale. In a nutshell, this might be a good entry point for a Data Scientist interested in upskilling and reskilling themselves to transition into a Data Engineering role.Pipeline-centric – This focus requires more in-depth knowledge working with more complex Data science needs. This type of role is found more often in mid-sized companies as they grow and incorporate a team of Data professionals to help analyze and offer actionable insight for the business. In a nutshell, this role creates a useful format for analysts to gather, collect, and analyze each bit of Data at each stage of development.Database-centric – This role is found most often in larger companies and deals not only with Data warehouses, but is focused on setting up analytics databases. Though there are some elements of the pipeline, this is more fine-tuned. In a nutshell, this role deals with many analysts across a wide distribution of databases. A Fine Balance Between Technical Skills, Soft Skills, and Business Acumen While it’s important for anyone filing this role to have deep knowledge of database design as well as a variety of programming languages, its equally important to understand company objectives. In other words, once the groundwork is laid and the datasets established, it’ll be important to explain what it is the business executives need to know to make the best decisions for their business.  Knowing how and what to communicate to executives, stakeholders, and your Data team also means understanding how to best retrieve and optimize the information for reporting. Depending on your organization’s size, you may need both a Data Analyst or Scientist and a Data Engineer. Though this is less likely in medium and larger enterprises. On the flip side, in order to understand the business’ needs, you’ll also need to be good at creating reliable pipelines, architecting systems and Data stores, and collaborating with your Data Science team to build the right solutions. Each of these skills are meant to help you understand concepts to build real-world systems no matter the size of your business. One Final Thought… Do you like to build things? Tweak systems? Take things apart and see how they work, then put them back together better and more efficient than before? Then Data Engineering might be for you. Are you a business who knows you’re ready to scale up and hire a Data professional? We have a strong candidate pool and may have just the person you need to fill your role. Are you a candidate looking for a role in big Data and analytics? We specialize in junior and senior roles. Check out our current vacancies or contact one of our recruitment consultants to learn more.  For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.   For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

The Harnham 2019 Data & Analytics Salary Guide Has Arrived

We are thrilled to announce the launch of our 2019 Data & Analytics Salary Guide. With over 1,500 respondents across the USA, this year’s guide is our largest and most insightful yet.  Looking at your responses, it is overwhelmingly clear that the Data & Analytics industry is continuing to thrive. This has led to an incredibly active market with 72% in the US willing to leave their role for the right opportunity.  Salary expectations remain high, although we’re seeing that candidates, on average, expect 10% more than they actually achieve when moving between roles.  We’ve also seen a change in the reasons people give for leaving a position, with a lack of career progression overtaking an uncompetitive salary as the main reason for seeking a change.   There also remains plenty of room for industry improvement when looking at gender parity; the US market is only 23% female, falling to 17% in Data Engineering roles and 16% in the Data Science space.  In addition to our findings, the guide also include insights into a variety of markets and recommendations for both those hiring, and those seeking a new role.  You can download your copy of the guide here.

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