Tag: analytics

29
Apr

PointPredictive is growing and looking for data scientists to join the team

PointPredictive Inc. is looking for data scientists to join our rapidly growing fraud and risk analytics team in sunny San Diego California.

At PointPredictive you will work with us solving complex fraud and risk problems for the financial services industry on emerging and growing areas of fraud risk.

What our Data Scientist Do At PointPredictive

Create predictive models based on big data sets using a variety of tools and algorithms in the cloud

Innovate in fraud and risk modeling by testing new statistical modeling techniques on data to boost predictive performance

Collaborate with our customers to analyze, mine, and aggregate their data into our fraud and risk data consortiums

Creatively solve fraud and risk problems in auto lending, mortgage, check, mobile, and electronic payments using internal and external data sources

Evaluate new data sets to determine their ability to boost detection performance

Collaborate with Fraud Consultants and others in the company to develop and launch new, innovative predictive analytic products to the market

Deliver recommendations to clients and the industry on how to best apply advanced analytics

Join our Team in San Diego

Our offices are open plan and modern and San Diego offers the best quality of life of any city in America.

Successful candidates for the position will have a Ph.D. (or a master’s degree with statistical modeling experience) in a technical field, preferably at least 2-4 years of experience working with data in industry, facility with modern programming languages (e.g. Python) in a Linux environment, familiarity with machine learning techniques (such as neural networks, random forests, deep learning), fluency with statistical and modeling packages such as R and ADAPA, and the proven ability to quickly build and deliver production ready statistical models to clients.

Successful candidates for the position will have a Ph.D. (or a master’s degree with statistical modeling experience) in a technical field, preferably at least 2-4 years of experience working with data in industry, facility with modern programming languages (e.g. Python) in a Linux environment, familiarity with machine learning techniques (such as neural networks, random forests, deep learning), fluency with statistical and modeling packages such as R and ADAPA, and the proven ability to quickly build and deliver production ready statistical models to clients.

We move fast at PointPredictive, so if this position sounds like an attractive or potential match for you, please email your CV or profile link today to fmckenna@pointpredictive.com

30
Jun

Deep Learning Presents Exciting Opportunities to Advance Analytics for Business

One of the most exciting recent developments in the area of predictive analytics is called deep learning.  Having already advanced the state of the art in many areas such as image recognition, deep learning is now poised to deliver cutting edge results for business application clients in the real estate, mortgage, and auto lending industries.

When building a statistical model, access to quality data is key.  But few data sets in industry are fed directly into a machine learning algorithm.  Instead, the raw fields are combined and transformed to produce features, which highlight important aspects of the data and problem being addressed to a model.  Features are also a way to incorporate human business expertise into a system.

One of the challenges with features is that their creation can be time consuming, and nowadays delivering a high performing, robust system quickly is especially important.  It is also difficult to be sure if a hand created feature set fully captures the data, and using more brute-force approaches to feature creation leads to a dependence on feature selection algorithms.

Enter deep learning.  By training and stacking multiple layers of Restricted Boltzmann Machines or auto-encoder neural networks, a system can now construct its own complex internal representations or features automatically.  And these layers can be built without the need for data tags or labels, which are frequently unavailable or incomplete in big, real world data sets.  Upon the newly learned features a more typical modeling technique such as logistic regression is then used to produce the final business score.

Here at Point Predictive Inc., deep learning is one of the approaches we use to build predictive features and models more quickly.  Not only faster, the transformations learned are not typically present in hand crafted feature sets, leading to more complete models.  When coupled with solution delivery in the cloud, deep learning is helping to deliver strong model performance within short project timelines.  Can deep learning play a role in your next analytic business application?