We are on a mission to disrupt the childcare industry. Applying the agility and ambitiousness of a start up to one of the few industrial sectors yet to be touched by the online revolution.

We need to identify the most investable areas in London to open new nursery (pre-school) sites (80 kids per site).  

A key part of the strategy is the development of a sophisticated tool that will enable us to quickly identify the best areas to invest across London (and later the key cities across the UK). 

The key pieces of data that we have for every pre-school in london (c1,750) are:  

- Post code

- Occupancy rates 

- Weekly fees 

- Number of students 

In addition we can pull data by borough or ward including 

- House price 

- Average earnings 

- Child births 

- Deprivation scores 

Looking for a capable data scientist to build us and easy to use tool, quickly and cost-effectively. 

We are also using geomaps; email me for access to the following link: 

https://www.google.com/maps/d/u/3/edit?mid=1IsisxcOznJF681lBJiPNmSc8zDI&authuser=3

Open to ideas for the best way to deliver. 

Preference is for applicants to be able to work on site in greater Boston area.

Metacog measures how learners tackle challenges within assessments, simulations, casual and serious games, and performance tasks. This is a lot more data than simply scores or grades.

We are looking for a Rockstar Data Scientist who can work with our educational data mining experts and development team to help them build engines that surface, analyze, score, and visualize learning pathways and patterns.

If you want to power the next generation of adaptive learning and continuous assessment solutions and provide learners with real-time, dynamic and interactive prescriptive guidance, talk to us. You will be instrumental in developing the next generation learning and assessment technologies that will be key in shaping the future of the U.S. Education System.

Responsibilities:

• Explore and advise on new data science techniques to take this industry to a new level.

• Develop, design and implement the company's Big Data system using AWS Cloud services.

• Design and implement data models developed by our learning scientists for deeper understanding around learning in interactive digital spaces, including educational games, simulations, and technology enhanced assessment items.

• Build and interpret probabilistic models of complex, highly-dimensional datasets from time series data.

• Write semantic classification algorithms using a combination of Python, Scala, Scalding, R, Julia etc.

• Organize, analyze, retrieve, classify, and report on student json clickstream and log data streams.

• Design and execute software at production scale by utilizing advanced feature generation, machine learning and data mining techniques to estimate learning efficacy and clusters of learning styles.

• Develop and/or assist in the creation of advanced Data visualizations to transform information into insights (like D3.js) These visualizations will be utilized for learning domain experts to identify how to tackle student misconceptions, as well as to discover opportunities to optimize emergent learning pathways.

• Apply advanced data and parallel programming frameworks (Spark or Map/Reduce) to effect real-world solutions. We seek a skilled practitioner who can also advance the state of the art.

• Present to all levels of the organization including peers, senior management, and external customers.

Desired Skills & Experience

• Experience with supervised and unsupervised machine learning and predictive techniques such as: Bayesian Classification, PCA, Ensembling, Decision Trees, Random Forests, Support Vector Machines, Neural Networks, Clustering and Deep Machine Learning, etc.

• 4+ years experience in analytical and algorithmic design for systems.

• Implementation of Big Data systems such as Hadoop, HBase, Spark, Shark, Storm, Cassandra, Kafka, Apache Drill, MemcacheD, etc. Experience using them in the cloud is a plus.

• Experience with visualization tools and creative data visualization (e.g. heat mapping, radial, and vector-based visualizations) especially for use by non-specialists and educators is a plus.

• Functional data-aware languages (Python, Mathematica or F#) are a strong plus.

• AWS, Big Data, Spark, Scala, Deep Machine Learning.

• 5+ years of application in a broad range of statistical concepts, including predictive and applied statistical modeling. Should have programming experience in Python, R, MPlus, Stata, Matlab, SPSS or other statistical packages.

• Masters (Math, Statistics, CS or physics preferred), Ph.D. or other pertinent industry experience preferred.

• Participation in Kaggle, KDD Cup (or other) competitions a plus.

Metacog seeks a Data Scientist/AWS Developer to reinvent the notion of what education is and extend the capabilities of our Big Data Learning Analytics team by integrating cutting edge data science into education products.

The education industry hasn’t changed much since the industrial era in the United States and the industry no longer creates the types of learners that 21st century employers need to hire. Nor does it produce the types of earning experiences that are engaging to this new generation of digital natives. Our $2.4 Trillion Skills Gap is the equivalent of a deep recession annually that holds back opportunity and potential for millions of learners and workers who are challenged with massive student debt and diminished prospects. These inefficiencies create serious challenges for U.S. competitiveness in a rapidly moving and increasingly automated global economy that has valued education highly as a “feeder system” for their economic engines and invested accordingly.

Metacog aims to fundamentally transform the U.S. Learning and Education industries by creating and making available a powerful game-changing big data analytics platform based on highly granular learner process data. This transformative technology has been built and made available via a series of APIs that companies building learning products can use to:

• Ingest and store massive streams of data exhaust as learners solve digital learning challenges.

• Automatically prove (and improve) the learning products being sold into the education system and determine which ones actually contribute towards effective learning to help improve accountability and optimize efficacy.

• Unlock next generation technologies like AR/VR that enable experiential, authentic and competency based learning technologies that have been shown to double teaching and learning effectiveness.

• Reduce (or eliminate) the reliance on excessive testing to assess learner progress, and instead offer “Invisible Integrated Assessment” into curriculum that can score and teach simultaneously at the point of learning in real-time.

We have done a consumer research study (survey) of consumers trying to learn about their perceptions of energy companies and what they (consumers) care about as it pertains to the way these companies operate.  

What we hope is to find interesting patterns of what people care about MOST and LEAST (maybe interesting combinations of the two?).

We also hope to be able to cluster respondendts in a meaningful way - as an example people who care about technology also care about the environment - or people who are very price sensitive don't care about what form their energy is produced in. 

So what we are looking for is a robust clustering analysis (including the visuals and the methods used) - and some descriptive analysis of the data from any interesting patterns that you see emerging or other (non-clustering) analysis that you want to use - for instance box plotting responses for questions - or looking at changes in the media across categories.

Here is the problem statment:

1. Given a set of feature vectors where each vector represents features found in a given column of data, cluster these columns into a set of discrete domains based on the similarity of the feature vectors. I'd like to use Latent Dirichlet Allocation as the clustering technique.

2. Next, given a set of graphs, where each graph represents a relational database schema, each node represents a table in a schema, and each edge represents a table relationship, construct a set of domain graphs. Table nodes contain one or more columns. Columns are mapped feature vectors. These were mapped to domains in step #1. Domain graphs are representations of the common domain relationships found in the source graphs. Each node is a domain. Each edge is a relationship derived from the underlying column table graph. The intent is for each of these domain-graphs to represent different commonly occurring conceptual data models. One model might represent the related domains found in an HR system representing an employee record. Another might represent customer information as found in a CRM system. And another might have elements representing both customers and employees such as what might be found in a Sales Compensation application database.

3. Refine the domain classification algorithm used in step one by considering adjacent domains found in one or more domain graphs. For example, compute the probability that a column having values ranging from 300 to 800 is a FICO score, given the column is part of a graph that commonly includes a FICO score data element. In other words, P (C1 = FICO-DOMAIN | C1 is part of DG1) is the probability, given that column C1 is part of domain graph DG1, and that DG1 has some probability of including a FICO-DOMAIN. We want to improve our ability to resolve columns having very similar features but represent very different data types. Columns having ranges of numeric values or timestamps are typically the most problematic.

4. Design a method using TensorFlow to combine independently derived domain-graph models (e.g. #2 above being computed in parallel by geographically distributed data profiling processes), such that the domain and domain graph representations (steps #1 and #3 above) eventually converge on a common set of domains and domain-graphs.

Deliverables

• The algorithms used to implement the solution
• Description of techniques used to implement steps 1 thru 4 in scope of work, such that they can be re-implemented independently by our internal team

Dataset Provided

• Feature vector dataset

Please provide your approach for the four problems above and tell us how many hours it will take you to complete this project.

We are seeking one to two experts to help us build the backend and frontend to an analytical application. This application performs a market basket analysis, letting people know what products were also bought in the “marketing basket” when another product was bought.  This app enables a user to select data available in a SaaS, get the data from a SaaS, run the data through an R model, and generate a related visualization that is displayed within a browser.  The R script is already written, and we have a working prototype for the visualization using D3.JS, but we desire your help to refine, extend, and complete this app.

As such, the work is as follows for the backend:

1. Take the current R script and review it for sufficiency and adequacy for our purpose, and then extend it to take data input in real-time from the SaaS using their API/IDE.

2. Create a server to host and run the R script when a request to do so comes from the SaaS. This server will need to scale to 100-1000 concurrent users.

3. Pass the results of the R script in JSON (or similar markup) to be used in the frontend for visualization (see below).

Related to the backend work is, of course, the frontend work, which should be a single, well-designed page that has dual functionality:

1. The frontend should have a page that allows for the user to select data from the SaaS. The functionality can be created using the SaaS’ API.

2. The frontend should render one or more visualizations of the JSON that results from the R-script.

To help us, in the backend, you will need to know R and have experience using a JAVA-based API and an understanding of javascript and JSON – and ideally using IDEs. To help us in the frontend, you will need to understand javascript/HTML and D3.JS.   Ideally we would like one person to do all this work, but understand that the skill sets may require two people to work together.

It is important that the front-end, which the user will see, be well-designed and aesthetically pleasing. If you feel like you can get us to the technical functionality we want, but don’t have the design skills, that’s okay!  We still want to talk, and can get a designer to help you.  

We are open to using alternative technologies – but prefer open source and free software. This opportunity, once successful, could yield many other projects from us, so we are looking for one or two people to work with in the long-term beyond this project.  For the right person/people, this will be a lucrative and career-building experience, working with well-known entrepreneurs and thought-leaders in the analytics space.

We are seeking one or two experts to help us take wireframes/visual design and business requirements and create an analytics application by 9/20/2016. This application will visualize the stages of the marketing funnel and pipeline information – and allow users to explore it by clicking and scrolling to explore the stages. We will go over the business requirements in more detail with selected candidates.  Note that this app is generates one visualization, which we have already designed, so we simply need to make the visualization interactive and integrated with the SaaS via their API and IDE.

To do this work we are looking for one to two people who can collaborate together to:

1. Take the sample data and the visual design and enable it to work with data retrieved from a SaaS API and IDE

2. Use a programming language, of your choice, such as JAVA or Python to create the logic and backend to drive the interactivity of the visualization.

3. Connect your backend to the SaaS in order to get data from it in real-time (when requested by users)

4. Use D3.JS or similar to take the visual design we have and make it work interactively to our business requirements using the backend you (or your partner) have created.

5. Create a frontpage in JS/HTML that guides the user in selecting/loading the data at first, and then when the data is loaded displays the visualization within the same page.

The final product will be a production-ready application that enable the user to access the frontpage upload/select their data, and then load their data, and view/interact with the resulting funnel visualization.  

It is important that the front-end, which the user will see, be well-designed and aesthetically pleasing. Fortunately for this project, we have already designed the visualization we want people to see as an output. Thus, if you feel like you can get us to the technical functionality we want, but don’t have the design skills, that’s okay!  We still want to talk, and can get a designer to help us take it over the finish line.   

We are open to using alternative technologies – but prefer open source and free software. This opportunity, once successful, could yield many other projects from us, so we are looking for one or two people to work with in the long-term beyond this project.  For the right person, this will be a lucrative and career-building experience, working with well-known entrepreneurs and thought-leaders in the analytics space.

As a Big Data consulting firm, we need to create a framework for providing our clients with Enterprise Information Architecture overall design services. This is an ongoing project as we need guidance and international level experience delivered to us on the subject.

We'd like someone with previous experience on EA & EIA preferably in Banking industry who is also familiar or have experience with Big Data in large enterprises.

The job entails creating a roadmap for our clients to implement big data within their organization. 

While this is a virtual position, candidates located in British Columbia and willing to travel to the Middle East would be preferred.

We are seeking part-time or full-time full time (can be negotiated) or part time instructors in New Yor City.

Apply Deep Learning techniques, primarily using TensorFlow to solve real-world business problems and extract insights and value from business data sets. Having completed 8 weeks of coursework and 4 weeks of project work, candidates will be highly employable as Data Scientists.  - See more at:https://www.experfy.com/training/courses/data-science-with-deep-learning-immersive#sthash.1rL3zuyX.dpuf

The following is required: 2 years experience in Machine Learning / Experience in Deep Learning and TensorFlow (or other frameworks like Theano). Required MS in Computer Science, desirable Ph.D. in computer science. 

The breakdown in terms of hours/rate for full time vs. part time is:

8hrs full time/4hrs partime (evenings after work)

Full time is $10k per month / part-time $100-$125/hr

Looking to start September during the 1st week. 

Medical Practice - Lasik Center is looking to better understand their target maret. 

Details:

• We need to analyze last 3 - 5 years of data to develop a patient profile / persona
• Conduct market segmentation analysis to unveil patient segments according to needs and demographics 
• Determine revenue potential of each patient segment so you can target them according to their profit potential.
• Determine sales and marketing channels most effective in targeting each segment (in process quantify the marketing ROI for each channel)
• Supplement the analysis with publicly available demographic and socio-economic data to determine the best site / location to open new locations.

Deliverable: 
Advisory Service Slide Deck / Excel  Data: 

Data will be provided in CSV format 

In your proposal please provide the amount of hours you estimate this project will require to be accomplished. 

Zodiac Aerospace is a world leader in aerospace equipment and systems for commercial, regional and business aircraft, as well as helicopters and space applications.

We are currently deploying a new BI tool (Birst) for our business. Some data models have been built, but due to our lack of internal resources, we need someone who can bring their expertise and quickly help build our models in the most effective and efficient manner. This consultant woud work in conjunction with our local ERP (Infor M3) experts to build these models. 

Can work remotely, but prefer an initial face to face for one-week at our office in Huntington Beach, California. We will pay for any reasonable travel expenses.  Intial engagement will be 4-6 weeks. Highly possible to longer/perpetual engagement if successul with the following deliverables. 

Deliverables include:

1. Robust data models for different functional spaces (Sales, Operations, Purchasing, Finance...)

2. Help build 4-5 critical reports within a 6 week period. 

Please talk about your specific experience doing this type of work.  We can review your Experfy profile for your background.

**Only US-based candidates may apply only for this position.**