Data Scientist - BioTech

Los Angeles Metro Area, California
US$150000 - US$200000 per year + Competitive Benefits

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Data Scientist - BioTech
Greater Los Angeles
$150,000 - $200,000 + Competitive Benefits

The Company

Harnham is working with a fast-paced, biotech company that encourages a start-up culture, with the benefits and stability of a well-established name in the industry. This company highly values data-driven solutions and is, in fact, one of the first of its kind to build out their Data Science team.

The team has grown exponentially over the past couple years and expect their team to grow into leadership roles as soon as possible! You will have direct impact within the team and organization, working closely with the executive team to reach business and product development goals. Additionally, you will work on expanding the team, supporting the hiring process and professional development of new talent.

If you're looking to work with people that care about what you say and believe in your potential - this is the place to be! This organization is transforming the biotech industry at a global scale - and this is your chance to join a team that will support your motivation to excel and work with tons of data!

The Role

You will:

  • Develop and optimize the Data Science infrastructure to be scalable and insightful, both internally and a production level
  • Demonstrate strong management and problem-solving skills - you will be taking initiative on important R&D to improve operation and products!
  • Communicate with organizational leaders across different verticals to deliver on data-driven goals, whether its your manager, engineers, marketing, or upper-echelon management!
  • Play a critical role in structuring the team's direction and scalability, from the challenges you will work on to the hiring process of future colleagues

Skills and Expertise

You have:

  • Dedicated knowledge in machine learning and the ability to produce object-oriented solutions (using C++ or Java) for logistical business strategy and product development
  • Expertise in R and/or Python, implementing machine learning techniques using python libraries, like TensorFlow, Scikit-learn, Pandas, and Keras
  • Exceptional communication skills - you can effortlessly relate insight to a team of engineers, business operations personnel, and an overseas data lake team!
  • Must have at least a Master's degree with 4 years of industry experience or a PhD with 2 years of experience, with a focus on Data Science or Machine Learning


$150,000 - $200,000 base pay

  • Competitive annual stock, 401K, and bonus opportunities
  • Relocation assistance
  • Phenomenal healthcare 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.

Python, R, Machine Learning, Deep Learning, Big Data, AI, Artificial Intelligence, Java, C++, Spark, AWS, SQL, Modelling, Algorithm, BioTech, Content Analysis, Sentiment Analysis, Data Scientist, Data Science, Scikit-learn, TensorFlow, PyTorch, Keras, Pandas, Classification, Clustering, Extraction

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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   For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to

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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