Predictive Analytics Jobs

The need for candidates who can use statistical techniques and predicative modelling to find patterns in historical and transactional data, is at an all-time high. The more you can understand about your customer and predict what their behaviour the more agile your business can be. 

This discipline crosses over many verticals such as retail, finance (credit risk and fraud) etc. mining data and extracting key information to predict trends. We are aware of the many applications for this profession, as well as the benefit to businesses who can use it to drive better decision making in the future.


TAKING THE GUESS WORK OUT OF PREDICTION

Brands who have a vested interest in the application of predictive analytics, know that we are a name to be trusted when sourcing that type of candidate who will foster a positive impact to business practices.

Leveraging the database we have developed over the years, we have a pool of talent that can empower any business, to more accurately evaluate trends and achieve more. Whether this is the start of the journey or not. We meet the needs of many different brands looking for a range of skill levels, from beginners to experienced analysts. 

For the East Coast and Mid-West teams please call 212-796-6070, or email newyorkinfo@harnham.com.

For the West Coast team call 415-614-4999 or email sanfraninfo@harnham.com.

Latest Jobs

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.

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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