Data Scientist




-Responsible for providing professional data, analytics, predictive modeling, and data visualization counsel to clients. Help clients understand, interpret and analyze massive data sets. Creatively apply data science to offer clients alternative approaches and solutions.

-Advise senior management in clear language about the implications of their work and discovered insights for the organisation.

-Display and visualize processed information so its value can be understood. Extract information from large datasets and present something of value to clients.

-Apply exploratory or predictive analytics to solve business problems.

-Perform data modeling. Implement and test data modeling designs. Use advanced math and statistics expertise using massive (beyond 500GB) of data. Use modern data analytical techniques working with information retrieval, machine learning, matrix and graph algorithms, unsupervised clustering & data mining to solve business problems.

-Must be able to apply advanced analytics, predictive modeling, and data visualization skills to different business situations.

-Ability to work with big datasets with minimal engineering support. Demonstrate analytical and problem solving skills, particularly those that apply to a Big Data environment. Ability to integrate research and best practices into problem avoidance and continuous improvement. Exercise independent judgment in methods, techniques and evaluation criteria for obtaining results.

Location: Client Sites

Status: Active

# of Positions Available: 4

Personality Traits

-They need to be very curious people, who enjoy diving deep into the material to find an answer to a yet unknown question.

-They need to have a natural desire to go beneath the surface of a problem.

-They need to be thinkers who can ask the right (business) questions.

-They need to be confident and self-secure as they more often then not will have to deal with situations where there is a lot unknown.

-They need to be patient as finding the unknown in massive data sets will take a lot of time and developing the algorithm to uncover new insights will often go by trial-and-error.

-They need to be able to see examples in totally different industries and be able to plot that on their current problem. For example, the Los Angeles Police department uses an algorithm to predict earthquakes to predict where crimes are likely to happen.

-Natural Language Processing: the interactions between computers and humans;

-Machine learning: using computers to improve as well as develop algorithms;

-Conceptual modelling: to be able to share and articulate modelling;

-Statistical analysis: to understand and work around possible limitations in models;

-Predictive modelling: most of the big data problems are towards being able to predict future outcomes;

-Hypothesis testing: being able to develop hypothesis and test them with careful experiments.

-Experience as a data scientist/analyst on large datasets, or data science research

-Worked with exploratory or predictive analytics

-Experience turning research ideas into actionable designs. Able to persuade stakeholders and champion effective techniques through product development.

-Significant experience working with Hadoop

-Experience in large-scale data development Experience with prediction or recommender systems, search and ranking algorithms, and classification algorithms

-Significant experience with machine learning

-Good understanding of design and architecture principles

-Experience working with large, unfiltered data sets'

-Strong verbal and written communication skills as well as presentation skills