UPLOAD YOUR CV
We help the best talent in the Digital analytics market to find rewarding careers.
Upload your CV and select your preferred disciplines so we can ensure your CV goes to one of our relevant specialist recruitersSend us your CV
Machine Learning Engineer - Deep Learning
San Francisco Bay Area
$180,000 - $220,000 + Competitve Benefits
Harnham is working with a late-stage venture that is paving the way for machine learning. This organization is looking to disrupt how supply chain analytics are performed and I can almost guarantee you know who they are. If you want to take your expertise to the big (data) table and join this company's advanced technology group - Let's talk.
You will be at the center of the action - working closely with executive leaders and management across different functions of the organization! With your remarkable experience implementing a technical and business acumen, you will have an opportunity to make major decisions in the infrastructure and direction of the team.
This organization will be working on revolutionary algorithms and deep learning is their bread and butter. You will be improving the way people interact online and in-stores. This organization is globally recognized and you will be part of that magic! They are not letting good talent fly by - and are investing all efforts to give the best experience to consumers and employees, alike.
Did I mention you get to work with machine learning legends?
Skills and Expertise
$180,000- $2200,000 + Competitive Benefits
*Open to Visa Sponsorship/Transfer!*
How to Apply
Please register your interest using the Apply button on this page.
For more information this role or other Data Science and Machine Learning roles, please contact Karla Guerra at Harnham.
Python, R, Machine Learning, Natural Language Processing, NLP, Recommendation Engine, Recommender System, Spark, AWS, GCP, Amazon, Google, SQL, Hadoop, Hive, Bigquery, HDFS, Flink, Beam, Kafka, MySQL, NoSQL, Cassandra, HBase, Bigtable, Shell, Perl, Bash, Ruby, Java, Scala, Travis CI, Jenkins, Deep Learning, Bayesian Statistics, Text Analytics, Thompson Sampling, Probability, Multi-Class, Decision Tree, Vector Machine, Reinforcement Learning, Reward System, Modelling, Algorithm, Content Analysis, Sentiment Analysis, Data Scientist, Data Science, Machine Learning, Machine Learning Engineer, Big Data, Scikit, Scikit-learn, TensorFlow, PyTorch, Keras, NumPy, CNN, RNN, NLTK, LSTM
US$180000 - US$200000 per year + Competitive Benefits
San Francisco, California
Harnham is working with a massive late-stage venture that is paving the way for machine learning. Let's talk about deep learning and/or ops research!
US$150000 - US$170000 per year + Equity + Benefits
Help shape the direction of this rapidly growing startup with your AI experience!
US$150000 - US$180000 per year + additional benefits
Los Angeles, California
Join a top medical imaging company and develop innovative solutions using deep learning and AI!
US$140000 - US$170000 per year + additional benefits
Join one of the top athletic brands in the world as a senior data scientist!
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.
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 firstname.lastname@example.org. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to email@example.com.
11. June 2019
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.
10. June 2019