Cummins Inc. Data Scientist 数据科学家 in Beijing, China
Data Scientist 数据科学家
Solves complex analytical problems using quantitative approaches through a combination of analytical, mathematical and technical skills. Researches, designs, implements and validates complex algorithms to analyze diverse sources of data to achieve targeted outcomes by leveraging complex statistical and predictive modeling concepts.
Participates in projects to support key objectives and business goals through the use of data science methodology.
Leverages data science methodology to solve complex business problems.
Creates multiple algorithms using complex statistical methodologies through the use of statistical programming languages and tools.
Partners with domain experts to verify model capabilities.
Partners with Solution Architect to enable appropriate data flow/data model, development using appropriate tools/technology, rapid prototyping and informs the design of analytical products.
Partners with less experienced employees on data science tools and methodologies.
Clearly articulates results, methodologies and learnings to stakeholder and peer group.
Continuous development and advancement of the team through knowledge sharing and collaboration.
Abstract Reasoning - Envisions a solution before implementation by analyzing data, extracting patterns and relationships to establish a problem or solution's feasibility; develops new algorithms and analytical models using process diagrams, flow charts, and textual documentation to explain or conceptualize a complex problem.
Data Mining - Identifies relationships and patterns in data by using a suite of data exploration and data visualization techniques using tools such as PowerBI, R Shiny, SAS JMP, and extracts insights into multivariate data by applying principles of multivariate data mining, small sample statistical inferential tests, dimension reduction techniques to understand the underlying structure of the data and enable sound conclusions upon model building.
Data Reduction - Performs data reduction in the context of data mining using variable selection techniques to maximize signal to noise ratio in large datasets for further predictive modeling.
Predictive Modeling - Develops statistical and machine learning models using appropriate variable transformations, feature selection strategies, imputation strategies, class rebalancing, resampling strategies and performance metrics to generate descriptive, explanatory or predictive models.
Data Smoothing and Filtering - Creates an approximating function to capture important features (low-frequency structures) while leaving out noise (high-frequency structures) in the data using various algorithms like moving-averages, robust aggregation schemes, robust regression schemes, fourier transforms and Kalman filters for signal processing and smoothening noisy time series data.
Statistical Foundations - Builds statistical explanatory models for regression, classification, outlier detection, anomaly detection, time series forecasting using knowledge of foundational statistics such as Null Hypotheses Significance Tests, regression models, generalized linear modeling, time series analysis, rank statistics, probability distribution fitting survival analysis, etc. to validate hypotheses or generate predictions for any given statistical or business question.
Text Analysis - Develops text analytics models using statistical techniques and natural language processing techniques such as word2vec, Latent Dirichlet Analysis (LDA), word frequency, sentiment analysis, key-phrase extraction, etc.to extract insights from, or build predictive models from unstructured text datasets.
Requirements Analysis - Evaluates relationships and interdependencies between requirements based upon their complexity and value to the business in order to determine feasibility and prioritization.
Programming - Creates, writes and tests computer code, test scripts, and build scripts using algorithmic analysis and design, Cummins IT processes, standard and tools, version control, and build and test automation to meet business, technical, security, governance and compliance requirements.
Tech savvy - Anticipating and adopting innovations in business-building digital and technology applications.
Balances stakeholders - Anticipating and balancing the needs of multiple stakeholders.
Collaborates - Building partnerships and working collaboratively with others to meet shared objectives.
Education, Licenses, Certifications
- College, university, or equivalent degree in statistics, information systems or related field required. PhD or Master’s degree in Statistics, Econometrics, Computer Science, or equivalent experience preferred
Minimum of 5 years of professional experience
Have Statistics, or Math, or Machine Learning Doctor degree.
Have much learning or study experience of academic research.
Job SYSTEMS/INFORMATION TECHNOLOGY
Primary Location China-Beijing-Beijing-China, Beijing, CUMMINS HQ
Job Type Experienced - Exempt / Office
Recruitment Job Type Exempt - Experienced
Job Posting Jun 8, 2020, 11:52:23 PM
Unposting Date Ongoing
Req ID: 200000XV