The Data Scientist independently plans, schedules, and leads data, analytics, and data science projects of moderate scope or portions of major large-scale projects and participates in the determination of technical objectives. Responsible for the department's data science work, including developing data models and ML algorithms, maintaining the analytics environment, and writing scripts for data integration and analysis. This hybrid role also assumes data engineering responsibilities, such as designing, implementing, testing, deploying, and maintaining stable, secure, and scalable data engineering solutions and pipelines. This role will work together with the rest of the Data & Analytics and other development teams to define requirements, mine and analyze data, integrate data from a variety of sources, and deploy high-quality data models in support of the analytics needs of the organization.
Role Responsibilities:
Proposes solutions and strategies to business challenges; Independently works on a wide range of complex problems that require new approaches through adaptation and modification of standard data, analytics, and data science principles, theories, techniques, and procedures, providing solutions that are imaginative, thorough, and practical.
Design, implement, test, deploy, and maintain stable, secure, and scalable data engineering solutions and pipelines in support of data and analytics projects, including integrating new sources of data into our central data warehouse and moving data out to applications.
Undertakes preprocessing of structured, semi-structured, and unstructured data
Analyzes large amounts of information to discover trends and patterns
Builds predictive models and machine-learning algorithms
Selects features, builds, and optimizes classifiers using machine-learning techniques
Combines models through ensemble modeling
Works with engineering and product development teams to deliver insights through predictive analytics
Performs data mining using state-of-the-art methods
Analyzes data, analytics, and BI problems to determine suitable solutions. Establishes and coordinates design reviews with peers and project leads. Responsible for organizing data and preparing documentation for assigned projects.
Responsible for thoroughly testing data, analytics, and machine learning solutions
Most work is examined at a higher level. Actual supervision received may be minimal or moderate, depending upon project complexity. Closer supervision is given on new aspects of assignments.
Qualifications:
Proven track record of applying data science techniques to solve complex problems.
Five years of experience coding in Python including comprehensive knowledge of Pythian libraries, applications and use cases for data prep and analysis (e.g. Pandas, NumPy )
Thorough knowledge of statistical methods and their applications
In-depth knowledge and expertise in machine learning algorithms and frameworks (e.g. TensorFlow, PyTorch, Scikit-learn)
Experience leading projects and mentoring junior data scientists or analysts.
Strong command of relational databases, SQL and ETL best practices
In-depth understanding of data lakes, data pipelines, and data science applications
Comprehensive knowledge of working with structured, semi-structured and unstructured data to develop and deploy predictive models and machine learning solutions
Ability to interpret moderately complex data, analytics, and data science requirements and apply data, analytics, and machine learning methodologies.
Proficient in general data manipulation tasks, including reading, processing, and cleaning data; transforming and recoding variables; merging multiple datasets; and reformatting data between wide and long formats
Demonstrated ability to learn new techniques and troubleshoot code without support; in other words, be able to learn on the job.
Use APIs to push and pull data from various data systems and platforms.
Demonstrated ability to write clear, well-documented code stored in a version control system.
Proven ability to work both independently and collaboratively within a team environment while following established procedures.
Excellent listening, interpersonal, communication, and problem-solving skills.
Preferred Qualifications:
Experience working for a large manufacturing company
Experience working for a large manufacturing company
General knowledge and understanding of business analysis best practices
Azure machine learning knowledge and experience
Familiar with best practices of data science application in manufacturing companies
General knowledge of Snowflake Snowpark and Cortex
Experience developing cloud data warehouses (e.g., Snowflake or Databricks)
Experience with lambda and kappa architectures
Experience with iPaaS platforms and other no-code/low-code platforms