Data Scientist

VWR (NASDAQ: VWR), headquartered in Radnor, Pennsylvania, is the leading, global, independent provider of products, services and solutions to laboratory and production facilities.

Apply machine learning and data mining technologies in developing innovative solutions which would include working with the technology team to support machine-learning algorithms in a big data platform to solve a variety of business problems. Defines problems, collects data, applies advanced economic and mathematical concepts, establishes facts and draws valid conclusions. Applies an indepth understanding of advanced mathematical concepts, operations and statistics to solve new and complex problems and develop innovative strategies.

* Analyze and model structured data using advanced statistical methods and implement algorithms and software needed to perform analyses * Build recommendation engines, sentiment analyzers and classifiers for unstructured and semi-structured data * Cluster large amount of user generated content and process data in large-scale environments using Hadoop and Spark * Perform machine learning, natural language, and statistical analysis methods, such as classification, collaborative filtering, association rules, sentiment analysis, topic modeling, time-series analysis, regression, statistical inference, and validation methods * Perform explanatory data analyses, generate and test working hypotheses, prepare and analyze historical data and identify patterns * Identifies and utilizes appropriate analytic methods and specialized tools to generate insights, answer the business questions and fulfill project objectives. Seeks out innovative approaches and techniques. * Prepares and delivers presentations on insights and provides recommendations for action. * Developing and optimizing algorithms to run in real-time/near real-time * Working with our technical teams to transition research into real-world products * Remaining current on emerging trends, tools, and technologies in the industry * Visualizing analysis of structured and unstructured data sets * Improving the scalability and performance of our existing analytics solutions * Conducting timely and effective research in response to specific requests (e.g. data collection, summarization, analysis, and synthesis of relevant data and information)

* Ability to aggregate, normalize and process data by authoring predictive algorithms to synthesize and present actionable data insights. * Experience in at least one of the following fields: machine learning, data visualization, statistical modeling, data mining, or information retrieval * Proficiency in analysis (e.g. R, SAS, Matlab) packages, programming languages (e.g. Java, Python, Ruby) as well as the ability to implement, maintain, and troubleshoot big data infrastructure, such as distributed processing paradigms, stream processing and databases such as Hadoop, Storm, SQL and Solr * Ability to solve complex problems in a fast-paced environment with limited guidance. * An eye for quality and a willingness to do what is necessary to achieve deadlines in a dynamic environment with frequent priority changes is required. * Able to work efficiently in teams and/or as an individual * Good oral and written communication skills. **Qualifications:** * Bachelor’s degree in Applied Statistics, Economics, Engineering, Mathematics, Finance, Computer Science, or Operations Research with 2-5 years applicable experience; or Master’s degree with 1-3 years applicable experience (preferred) * 1 to 3 years’ experience in advanced analytics, model building, statistical modeling, optimization, and machine learning algorithms including supervised and unsupervised learning, boosting and ensemble methods **Environmental Working Conditions & Physical Effort:** * Typically works in an office environment with adequate lighting and ventilation and a normal range of temperature and noise level. * Work assignments are diversified. Examples of past precedent are used to resolve work problems. New alternatives may be developed to resolve problems. * A frequent volume of work and deadlines impose strain on routine basis. * Minimal physical effort is required. Work is mostly sedentary but does require walking, standing, bending, reaching, lifting or carrying objects that typically weigh less than 10 lbs.