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Our client has a brand new opportunity for a qualified Data Scientist to join their team for an onsite position in Seattle, WA. This resource will play a crucial role in advancing the client's data-informed decision-making processes by applying advanced statistical, analytical, and machine learning techniques to complex institutional data. This role will be responsible for technical development and deployment end-to-end analytical lifecycles. The Data Scientist will develop, improve, and maintain data models, code, and performance of production level data models to ensure timely, accurate, and sustainable analyses. The Data Scientist plays a collaborative role through the continuous improvement of data ingestion, standardization, reporting and interpretation of predictive and descriptive analytics.
Data Analytics Life Cycle :
Model Design: Translate business requirements and stakeholder requests into technical requirements
Evaluate technical requirements to identify data requirements and / or gaps
Evaluate the appropriateness of analytical approaches and advise on best practices for model identification in coordination with the Lead Data Scientist
Collaborate with data stewards, system subject matter experts, and stakeholders to ensure data accuracy and identify data ingestion pipelines and improvement
Develop subject matter expertise for critical or Client data models or systems
Automate and integrate ad hoc analyses
Data Engineering: Develop initial analytical models to extend or enhance production models
Independently or in partnership with the BI Engineer, create data pipelines for data ingestion
Transform data for model ingestion using available frameworks such as AWS, CSV files, and databases
Profile data to explore and validate data content, structure, and establish error detection functions
Support strategic initiatives and special projects through continuous improvement and automation of prototype and production data models and analyses
Model Engineering: Execute algorithms and processes to obtain key findings
Develop standard procedures to write, train, and test appropriate analytical or machine learning models including feature engineering and hyperparameter tuning
Collaborate with Lead Data Scientist to ensure the model meets original objectives and address any questions about data and methodological validity
Document metadata, transformations, and pipeline processes
Analytical Operations: Deploy analytical models into production environment
Prepare the model artifact for production environment
Ensure accurate version control of code and data processes ensuring that modification or enhancements are through continuous improvement / continuous development pipelines
Establish and automate model performance monitoring and re-training triggers
Provide training and ongoing support to users of analytical products
Analytical Findings: Provide timely interpretation and validation of predictive models to stakeholders
Design and create data visualizations, dashboards, scorecards, and reports to effectively communicate complex insight from models to client stakeholders
Collaborate with Lead Data Scientist to ensure findings meet statistical and internal standards
Collaborate with BI developer to integrate data or findings into existing reporting or new reports
Collaborate with the Director of Decision Support to ensure stakeholders have sufficient understanding of methodology and findings to guide decision making process
Relationship Management: establishing and maintaining relationships across the team and cross-functionally
Management of competing priorities and expectations across different teams and stakeholders
Effective communication and collaboration skills, especially in working with cross-functional teams and staff across diverse institutional research initiatives
Collaborate across the Decision Support team and cross-functionally to solicit feedback and continuously improve operations and analytical products
Survey Data and Analysis:
Collaborate with client users to improve survey design, administration, and analysis
Act as client Qualtrics brand administrator
Research and Innovation :
Stay up to date with best practices and emerging trends in data science, institutional research, Client operations and higher education, and recommend changes to analytical methodologies and tools as needed to ensure the client remains competitive and effective
Skills and Qualifications:
Advanced understanding of data analysis including descriptive statistics, predictive and explanatory statistical modeling techniques, and data mining or embedded analytics techniques, typically requiring a graduate degree in statistics or a statistical social science field or equivalent experience
Minimum 5 years of professional work experience in higher education
Knowledge of the data sources, metrics, and analysis best practices in higher education
Ability to lead and collaborate with faculty and staff across a comprehensive set of institutional research initiatives and projects
Proficiency programing with Python or R with experience writing efficient and optimized code
Knowledge of machine learning algorithms, including supervised and unsupervised learning techniques such as regression, decision, trees, random forests, clustering algorithms, and neural networks
Understanding of the strengths and limitations of statistical and machine learning algorithms for model selection and optimization
Expertise in data preprocessing techniques, such as data cleaning, normalization, and feature scaling
Proficiency in evaluating and validating machine learning models using appropriate performance metrics, cross-validation techniques, and methods like precision, recall, ROC-AUC, and confusion matrices is crucial. Experience in tuning hyperparameters to optimize model performance is also important
Experience with data warehousing and relational databases including big data processing frameworks like Apache Spark or AWS as well as managing large-scale datasets using SQL or Hadoop
Knowledge of data visualization and executive dashboard development techniques
Ability to solve problems, work effectively independently and as a part of a team, manage multiple short- and long-range projects simultaneously, and design and conduct institutional research studies
Ability to interpret and apply federal and state regulations and Client policies governing the appropriate use and dissemination of student, employee, and institutional data
Strong oral and written communication skills
Proficiency with MS Office software including Excel, Access, Word, and PowerPoint
Strong attention to detail and accuracy
Strong interpersonal skills
Positive attitude and ability to work with individuals from a variety of backgrounds and ability levels
$45.00 per hour
Full COVID-19 vaccination may be required.
We offer a comprehensive benefit package that you can elect into including but not limited to: Health Insurance (Medical, Vision, Dental), 401k, Basic Life/AD&D, Supplemental Insurances Plans, Paid Time Off Plan, Paid Holiday, Paid Sick Leave plan, FSA/HSA Pre-Tax Benefits, Employee Discounts.
W2 only, no Corp to Corp. We are unable to sponsor H1B visas at this time. eX cell Supports Equal Employment Opportunity e X cell , a division of CompuCom Systems, Inc., a global company headquartered in Bellevue, Washington, provides IT staffing services and solutions to Fortune 1000 companies as well as small and medium business. For more information, visit .
Full Time
$125k-153k (estimate)
05/01/2024
05/20/2024
excellgroup.com
PAMPISFORD, ENGLAND
200 - 500
1992
DEAN LEROY HILLS
<$5M
The following is the career advancement route for Data Scientist positions, which can be used as a reference in future career path planning. As a Data Scientist, it can be promoted into senior positions as a Data Scientist IV that are expected to handle more key tasks, people in this role will get a higher salary paid than an ordinary Data Scientist. You can explore the career advancement for a Data Scientist below and select your interested title to get hiring information.
If you are interested in becoming a Data Scientist, you need to understand the job requirements and the detailed related responsibilities. Of course, a good educational background and an applicable major will also help in job hunting. Below are some tips on how to become a Data Scientist for your reference.
Step 1: Understand the job description and responsibilities of an Accountant.
Quotes from people on Data Scientist job description and responsibilities
Data scientists work closely with business stakeholders to understand their goals and determine how data can be used to achieve those goals.
12/22/2021: Dallas, TX
Data scientists are meant to use their technology and social science skills to develop different trends and manage data wisely.
02/17/2022: Lompoc, CA
A data scientist develops software to structure the raw data.
02/16/2022: Lansing, MI
Data scientist will need to meet with their business stakeholders early and often to ensure that they are on the same page about the goals and deliverables of the project.
02/12/2022: San Diego, CA
Data scientists may spend some of their time working on ad hoc data requests, but these types of requests should only take up a small portion of their time.
02/01/2022: Peoria, IL
Step 2: Knowing the best tips for becoming an Accountant can help you explore the needs of the position and prepare for the job-related knowledge well ahead of time.
Career tips from people on Data Scientist jobs
Analyzing data from multiple angles and searching for trends that could reveal problems or opportunities.
01/31/2022: Passaic, NJ
Data scientists lay a solid foundation to help perform all kinds of analysis.
01/31/2022: Champaign, IL
Data scientists are also expected to have stronger software engineering skills that data analysts.
02/19/2022: Springfield, OR
Business analysts are focused on reporting, just like data analysts.
02/04/2022: Jamestown, NY
A data scientist should understand the assumptions that need to be met for each statistical test.
03/04/2022: Hialeah, FL
Step 3: View the best colleges and universities for Data Scientist.