Lead II - Data Science

Role Proficiency

Independently provides expertise on data analysis techniques using software tools; streamlining business processes and managing team

Outcomes

 

• Managing and designing the reporting environment including data sources security and metadata.
• Providing technical expertise on data storage structures data mining and data cleansing.
• Supporting the data warehouse in identifying and revising reporting requirements.
• Supporting initiatives for data integrity and normalization.
• Assessing tests and implementing new or upgraded software and assisting with strategic decisions on new systems.
• Synthesize both quantitative and qualitative data into insights
• Generating reports from single or multiple systems.
• Troubleshooting the reporting database environment and reports.
• Understanding business requirements and translating it into executable steps for the team members.
• Identify and recommend new ways to streamline business processes
• Illustrates data graphically and translates complex findings into written text.
• Locating results to help the clients make better decisions. Get feedback from clients and offer to build solutions based on the feedback.
• Review the team’s deliverables before sending final reports to stakeholders.
• Support cross-functional teams with data reports and insights on data.
• Training end users on new reports and dashboards.
• Set FAST goals and provide feedback on FAST goals of reportees
  
   

Measures Of Outcomes

 

• Quality - number of review comments on codes written
• Accountable for data consistency and data quality.
• Number of medium to large custom application data models designed and implemented
• Illustrates data graphically and translates complex findings into written text.
• Number of results located to help clients make informed decisions.
• Attention to detail and level of accuracy.
• Number of business processes changed due to vital analysis.
• Number of Business Intelligent Dashboards developed
• Number of productivity standards defined for project
• Manage team members and review the tasks submitted by team members
• Number of mandatory trainings completed
  
   

Outputs Expected

Determine Specific Data needs:

 

• Work with departmental managers to outline the specific data needs for each business method analysis project
  
   

Management And Strategy

 

• Oversees the activities of analyst personnel and ensures the efficient execution of their duties.
  
   

Critical Business Insights

 

• Mines the business’s database in search of critical business insights and communicates findings to the relevant departments.
  
   

Code

 

• Creates efficient and reusable SQL code meant for the improvement manipulation and analysis of data.
• Creates efficient and reusable code. Follows coding best practices.
  
   

Create/Validate Data Models

 

• Builds statistical models; diagnoses validates and improves the performance of these models over time.
  
   

Predictive Analytics

 

• Seeks to determine likely outcomes by detecting tendencies in descriptive and diagnostic analysis
  
   

Prescriptive Analytics

 

• Attempts to identify what business action to take
  
   

Code Versioning

 

• Organize and manage the changes and revisions to code. Use a version control tool like git bitbucket. etc.
  
   

Create Reports

 

• Create reports depicting the trends and behaviours from the analysed data
  
   

Document

 

• Create documentation for own work as well as perform peer review of documentation of others' work
  
   

Manage Knowledge

 

• Consume and contribute to project related documents share point libraries and client universities
  
   

Status Reporting

 

• Report status of tasks assigned
• Comply to project related reporting standards/process
  
   

Skill Examples

 

• Analytical Skills: Ability to work with large amounts of data: facts figures and number crunching.
• Communication Skills: Communicate effectively with a diverse population at various organization levels with the right level of detail.
• Critical Thinking: Data analysts must look at the numbers trends and data and come to new conclusions based on the findings.
• Presentation Skills - reports and oral presentations to client
• Strong meeting facilitation skills as well as presentation skills.
• Attention to Detail: Making sure to be vigilant in the analysis to come to correct conclusions.
• Mathematical Skills to estimate numerical data.
• Work in a team environment
• Proactively ask for and offer help
  
   

Knowledge Examples

Knowledge Examples

 

• Database languages such as SQL
• Programming language such as R or Python
• Analytical tools and languages such as SAS & Mahout.
• Proficiency in MATLAB.
• Data visualization software such as Tableau or Qlik or Power BI.
• Proficient in mathematics and calculations.
• Spreadsheet tools such as Microsoft Excel or Google Sheets
• DBMS
• Operating Systems and software platforms
• Knowledge about customer domain and also sub domain where problem is solved
  
   

Additional Comments

Senior Data Scientist for eComm Analytics Description: UST Global® is looking for a highly energetic and collaborative Senior Data Scientist with experience building enterprise level GenAI applications, designed and developed MLOps pipelines . The ideal candidate should have deep understanding of the NLP field, hands on experience in design and development of NLP models and experience in building LLM-based applications. Excellent written and verbal communication skills with the ability to collaborate effectively with domain experts and IT leadership team is key to be successful in this role. We are looking for candidates with expertise in Python, Pyspark, Pytorch, Langchain, GCP, Web development, Docker, Kubeflow etc. Key Responsibilities:

 

• Work with AI/ML Platform Enablement team within the eCommerce Analytics team. The broader team is currently on a transformation path, and this role will be instrumental in enabling the broader team's vision.
• Work closely with other Data Scientists to help with production models and maintain them in production.
• Deploy and configure Kubernetes components for production cluster, including API Gateway, Ingress, Model Serving, Logging, Monitoring, Cron Jobs, etc. Improve the model deployment process for MLE for faster builds and simplified workflows
• Be a technical leader on various projects across platforms and a hands-on contributor of the entire platform's architecture
• Responsible for leading operational excellence initiatives in the AI/ML space which includes efficient use of resources, identifying optimization opportunities, forecasting capacity, etc.
• Design and implement different flavors of architecture to deliver better system performance and resiliency.
• Develop capability requirements and transition plan for the next generation of AI/ML enablement technology, tools, and processes to enable to efficiently improve performance with scale. Tools/Skills (hands-on experience is must):
• Ability to transform designs ground up and lead innovation in system design
• Deep understanding of GenAI applications and NLP field
• Hands on experience in the design and development of NLP models
• Experience in building LLM-based applications
• Design and development of MLOps pipelines
• Fundamental understanding on the data science parameterized and non-parameterized algorithms.
• Knowledge on AI/ML application lifecycles and workflows.
• Experience in the design and development of an ML pipeline using containerized components.
• Have worked on at least one Kubernetes cloud offering (EKS/GKE/AKS) or on-prem Kubernetes (native Kubernetes, Gravity, MetalK8s)
• Programming experience in Python, Pyspark, Pytorch, Langchain, Docker, Kubeflow
• Ability to use observability tools (Splunk, Prometheus, and Grafana ) to look at logs and metrics to diagnose issues within the system.
• Experience with Web development Education & Experience: -
• 6+ years relevant experience in roles with responsibility over data platforms and data operations dealing with large volumes of data in cloud based distributed computing environments.
• Graduate degree preferred in a quantitative discipline (e.g., computer engineering, computer science, economics, math, operations research).
• Proven ability to solve enterprise level data operations problems at scale which require cross-functional collaboration for solution development, implementation, and adoption.

Desired Skills and Experience

Python,Pyspark,Pytorch,Docker