Data Science

What We Do

We help the best talent in the Data Science market find rewarding careers.

In the US today, “Data-driven” businesses have a huge advantage over their competition and are generating massive impact. As such, the ability to understand how to extract actionable insights from large volumes of structured or unstructured data through the application of Machine & Deep Learning models is one of the most sought-after skillsets for employers. Data science requires those who work in the field to often write sophisticated algorithms that extract insights from large and complex data sources.

Those with a strong problem-solving ability and a team orientated focus, combined with a desire to generate real business impact, along with proficiency across more than one data science discipline, such as machine learning, NLP, Deep-Learning and statistics will flourish within the right role. It is our job to find that right fit for you

HOW We Do IT

We comprehend the myriad of technical disciplines and transferable skills a data scientist needs to be proficient across,
which set apart good candidates from the exceptional.

Our dedicated teams can spot this talent to supply our clients with people that can keep their business one step ahead. We pride ourselves on keeping on top of college programs who are upskilling the next generation of data scientist professionals.

Understanding the parallels between this and the evolution of big data, and high-performance computing, from entry level to senior positions.

What sets us apart?

Our specialty is matching highly experienced and skilled talent, with world leading organisations and disruptive start-ups who value the hidden insights data scientists can extract from their data.

Uniquely, we understand the bespoke nature of this requirement and specialism, which goes beyond just qualifications in languages and database technologies such as R, Python, Spark & Tensorflow. If you looking to add some scientific insight to a team and drive a business forward contact our Data Science Team.

Latest Jobs

Salary

US$200000 - US$250000 per annum + Amazing Benefits

Location

New York

Description

A global technology company worth over $10 billion is looking for a principal software engineer to work on ad-tech for their platform.

Salary

US$165000 - US$185000 per annum + Amazing Benefits

Location

Washington, District of Columbia

Description

Innovate machine learning position using their expertise in NLP to find trends around the world and identify major correlations on what will happen next

Salary

US$20000 - US$220000 per annum + bonus, benefits, unlimited vacation

Location

New York

Description

Looking for a principal analytics consultant to come innovate and strategize the way this legendary healthcare company enters the retail space.

Salary

US$90000 - US$110000 per annum

Location

District of Columbia

Description

A public health action-tank that helps vulnerable populations across the globe are looking for a statistician focusing on causal inference

Salary

US$170000 - US$190000 per annum + bonus and benefits

Location

Philadelphia, Pennsylvania

Description

Looking for an Associate Director to lead research and innovation with pharma! Must have a deep understanding of data science and strong business acumen.

Salary

US$180 - US$200 per annum + Bonus

Location

San Francisco, California

Description

Develop Machine Learning models & frameworks to address core business problems and create actionable insights

Salary

US$220000 - US$240000 per annum + bonus and benefits

Location

Irvine, California

Description

Looking for a Director of Predictive Analytics to build an analytics organization within a national market leader! Positions reports directly to CFO.

Salary

US$220000 - US$240000 per annum + bonus, benefits, unlimited vacation

Location

New York

Description

Looking for a Director of Analytics to come innovate and strategize the way this legendary healthcare company enters the retail space.

Salary

US$150000 - US$160000 per annum

Location

New York

Description

This is an opportunity for a data scientist to join a fast-growing and innovative private equity firm.

Salary

US$180000 - US$200000 per annum + Bonus + Benefits

Location

New York

Description

This is a senior level position where you'll have the opportunity to work on the analytics behind some of the most popular video games available today!

Salary

US$165000 - US$185000 per annum

Location

Washington, District of Columbia

Description

Innovate machine learning position using their expertise in NLP to find trends around the world and identify major correlations on what will happen next.

Salary

US$220000 - US$240000 per annum + bonus and benefits

Location

Cupertino, California

Description

Looking for a principal data scientist to lead the innovation towards better health via behavioral hacking using deep learning!

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 Slam-Dunk Career as a SLAM Engineer

Philadelphia. It’s known for it’s Philly Cheesesteak, the Liberty Bell, and where the Constitution was signed. Always on the cutting edge, Philadelphia is a land of firsts. You may or not know this, but one of its firsts was to have the first general use computer in 1946. Is it any wonder then that a company there is building robots to navigate GPS denied environments and was begun by leaders in the Computer Vision space?  Beyond the Roomba If you consider the Roomba, the autonomous vacuum that sweeps up pet hair, dirt, and other unwanted product, how does it know where to go? How does it know to go under a table or chair or around a wall to the next room? How does it know to avoid the dog, cat, or you? On nearly the smallest scale, this little round machine is a personal version of simultaneous location and mapping (SLAM).  However, the computational geometry method of this mapping and localization technique extends in a wide variety of arcs. Here are a few to get you thinking: GPS Navigation SystemsSelf-driving carsUnmanned Aerial Vehicles (UAV)Autonomous Underwater Vehicles (AUV)DronesRobotsVirtual Reality (VR)Augmented Reality (AR)Monocular Camera...and more There’s even a version which is used in the Life Sciences called RatSLAM. But we’ll visit that in another article. The uses and benefits of this simultaneous location and mapping technique are exponential even with some of the challenges posed by Audio-Visual and Acoustic SLAM. What is SLAM? Essentially, it is the 21st century version of cartography or mapping. Except in this case, not only can it map the environment, but it can also locate your place in it. When you want to know where the nearest restaurant is, you simply type in ‘restaurant near me.’ And soon, a list appears on your phone with a list radiating from nearest location outward.  Imagine you’re lost on a hike, you manage to find signal, and soon your GPS is offering directions on which way to move toward civilization.  This is Simultaneous Localization and Mapping. It locates you, your vehicle, a robot, drone, unmanned aerial vehicle or self-driving car and puts people and things in the direction it thinks they want to go or should go to get to safety. While mapping is at the epicenter of SLAM Computer Vision Engineering, there are other elements within the field as well. But let’s begin with mapping. Topological maps offer a more precise representation of your environment and can therefore help ensure consistency on a global scale.  Just as humans do when giving directions, sensor models offer landmark-based approaches to make it easier to determine your location within the map’s structure and raw-data approaches which makes no assumptions. Landmarks such as wifi or radio beacons are some of the easiest to locate, but may not always be correct which is where the raw-data approach comes in to offer its two cents as a model of location function. Four Challenges of SLAM GPS sensors may not function properly in chaotic environments such as military conflict. }Non-static environments such as pedestrians or high traffic areas with multiple vehicles make locations difficult to pinpoint.In Acoustic SLAM, challenges include inactivity and environmental noise as well as echo. Sound localization requires a robot or machine to be equipped with a microphone in order to go in the requested direction. Five Additional Forms of SLAM Tactile (sensing by touch)RadarAcousticAudio-Visual (a function of Human-Robot interaction)Wifi (sensing strength of nearby access points) Ready to Explore a Robotics and Computer Vision Career? Whether you’re interested in a slam dunk career as a SLAM Engineer or looking for your first or next role in Big Data, Web Analytics, Advanced Analytics & Insight, Life Science Analytics, or Data Science, take a look at our current vacancies or get in touch one of our expert consultants to learn more.   For our West Coast Team, contact us at (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.   For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to newyorkinfo@harnham.com.

How Machine Learning and AI Can Help Us See the Forest for the Trees

In the early days of 2020, Johns Hopkins, the CDC, the WHO, and a host of other public organizations banded together in collaboration. They were on a mission to ensure the world had real-time information to a virus that would forever chance the course of this year and the years to come. Which is great for those families with a computer in every home or every person with smartphone access. But what about the rest of the world? How do you ensure those people without access to basic needs lives can be improved? A health non-profit using AI and Machine Learning is aiming to do just this. But the Data is vast and the sheer numbers of people need to be corralled by someone into something the computers can read and make decisions on. Who would have thought Public Research and Data Science would come together in such a manner and in such an important time? Three Benefits of Data Science and Machine Learning in Healthcare According to a seminar given in September 2019, two research scientists explained to the CDC the promises and challenges using Big Data for public health initiatives. After explaining a few definitions and making correlations, the focus was soon on the benefits. The focus of Machine Learning is to learn data patterns.From the initial focus, patterns can then be validated to ensure they make sense.These patterns and validation of patterns can find links between seemingly uncorrelated factors such as the relationship between one’s environment and their genetics. To the scientists working with these scenarios, the decisions seem simple. Yet, when it comes to explaining them to laymen like policymakers, there can be a shift in understanding. This shift can lead to arbitrary and different findings which can affect medical decision making. Why? Could it be using Random Forests in linking the data could be confusing?  Data Classification is Not as Cut-and-Dried as a Work Flow or Org Chart If someone shows us a work flow or organizational chart, we understand immediately each task to be done in which order or who reports to whom. But in trying to link uncorrelated bits of information using decision trees, it can seem more like abstract art, more subjective than direct. Yet, it is those correlations which answer the bigger questions brought to bear by Research Scientists, Public Health Researchers, the Data Scientists, and AI working together to see the bigger picture. Decision trees, ultimately, are the great classifier. But there are a few things which need to be in place first. Yet, in the random forest model it’s not just one decision tree, it’s many. This is definitely a case where, if you done right, you will see the forest for the trees and at the same time be able to determine patterns in those trees. A bit counter-intuitive, but this is what stretches our minds to see correlations and patterns we might not see otherwise, don’t you think? So, what do you need to help make predictions?  Two Important Needs to Help Make Predictions Predictive power. The features you employ should make some sense. For example, without a basic knowledge of cooking, you can’t just throw random items from your refrigerator into a pot and expect it taste good. Unless of course, you’re making soup and all you have to do is add water.The trees and their predictions should be uncorrelated. If you’ve ever seen M. Night Shymalan’s Lady in the Water, there’s a little boy who can ‘read’ cereal boxes and tell a coherent story. A predictive coherent story. This is the layman’s version of random forests, their predictive nature, and ultimately, the scientists who can ‘read’ and explain the patterns. If you're looking for your first or next role in Big Data, Web Analytics, Marketing & Insight, Life Science Analytics, and more, check out our current vacancies or contact one of our recruitment consultants to learn more.   For our West Coast Team, contact us at (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.   For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to newyorkinfo@harnham.com.  

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