Role Proficiency:

Independently interprets data and analyses results using statistical techniques

Outcomes:

      Independently Mine and acquire data from primary and secondary sources and reorganize the data in a format that can be easily read by either a machine or a person; generating insights and helping clients make better decisions.       Develop reports and analysis that effectively communicate trends patterns and predictions using relevant data.       Utilizes historical data sets and planned changes to business models and forecast business trends      Working alongside teams within the business or the management team to establish business needs.       Creates visualizations including dashboards flowcharts and graphs to relay business concepts through visuals to colleagues and other relevant stakeholders. Set FAST goals

Measures of Outcomes:

      Schedule adherence to tasks       Quality – Errors in data interpretation and Modelling       Number of business processes changed due to vital analysis.       Number of insights generated for business decisions       Number of stakeholder appreciations/escalations       Number of customer appreciations No: of mandatory trainings completed

Outputs Expected:

Data Mining:

Acquiring data from various sources

Reorganizing/Filtering data:

Consider only relevant data from the mined data and convert it into a format which is consistent and analysable.

Analysis:

Use statistical methods to analyse data and generate useful results.

Create Data Models:

Use data to create models that depict trends in the customer base and the consumer population as a whole

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 with project related reporting standards and process

Code:

Create efficient and reusable code. Follows coding best practices.

Code Versioning:

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

Quality:

Provide quality assurance of imported data
working with quality assurance analyst if necessary.

Performance Management:

Set FAST Goals and seek feedback from supervisor

Skill Examples:

      Analytical Skills: Ability to work with large amounts of data: facts figures and number crunching.       Communication Skills: Ability to present findings or translate the data into an understandable document       Critical Thinking: Ability to look at the numbers trends and data; coming up with new conclusions based on the findings.       Attention to Detail: Making sure to be vigilant in the analysis to come with accurate conclusions.       Quantitative skills - knowledge of statistical methods and data analysis software       Presentation Skills - reports and oral presentations to senior colleagues       Mathematical skills to estimate numerical data.       Work in a team environment Proactively ask for and offer help

Knowledge Examples:

Knowledge Examples

      Proficient in mathematics and calculations.       Spreadsheet tools such as Microsoft Excel or Google Sheets       Advanced knowledge of Tableau or PowerBI       SQL       Python       DBMS       Operating Systems and software platforms       Knowledge about customer domain and also sub domain where problem is solved Code version control e.g. git bitbucket etc

Additional Comments:

Key Responsibilities • Create and refine digital twin solutions that accurately mirror physical assets and processes in a digital format. • Collaborate with cross-functional teams such as testing, DevOps, and Customer Solutioning to gather requirements and convert them into actionable technical designs. • Construct and manage data pipelines and integration workflows to support digital twin models. • Leverage cloud platforms like Azure or AWS to deploy, manage, and scale digital twin solutions. • Produce and maintain robust Python code for data handling, whether as APIs or standalone applications, alongside analytics and simulation tasks. • Integrate real-time data from IoT devices and sensors into the digital twin models, enhancing their accuracy and functionality. • Guarantee the security, scalability, and efficiency of the digital twin solutions. • Specialize in implementing serverless concepts (Azure Functions, Lambda), ML inferencing. • Stay informed of the latest advancements in digital twin technology, cloud computing, and data engineering. Required Skills and Qualifications • A Bachelor’s degree in Computer Science, Engineering, or a related discipline. • At least 3 years of experience in data engineering and handling large datasets. • Strong skills in cloud platforms (Azure or AWS) and their associated services. • Proficiency in Python programming. • Experience with data processing tools and frameworks such as Apache Spark, Databricks, or Apache Kafka. • Understanding of IoT technologies and protocols, for example, MQTT and OPC UA. • Exposure to machine learning frameworks and MLOps practices such as Scikit-learn, TensorFlow, and PyTorch is advantageous. • Familiarity with containerization and orchestration tools like Docker and Kubernetes. • Knowledge of microservices architecture and the creation of RESTful APIs. • Strong problem-solving abilities, capable of working autonomously and within a team. • Excellent communication skills, adept at explaining complex technical ideas to non-technical stakeholders. Tools and Frameworks • Cloud Platforms: Microsoft Azure, AWS • Cloud Services: Azure Function App, Lambda, CosmosDB, App Service, ADX, AKS, Networking concepts, Storage Account • Data Processing: Apache Spark, Databricks, Apache Kafka, AirFlow, KServe • Programming Languages: Python • Message Brokers: Azure EventHub, Azure ServiceBus, Azure EventGrid, AWS services, MQTT brokers, Kafka • IoT: MQTT, OPC UA, Azure IoT Hub, AWS IoT Core • DBMS: SQL, Influx, MongoDB • Machine Learning: TensorFlow, PyTorch (optional), with emphasis on Computer Vision, Data Processing, Data Visualization • Containerization: Docker, Kubernetes • APIs: RESTful APIs using Flask or FastAPI • Visualization: Power BI, Tableau, D3.js, Grafana • DevOps: Git, Azure DevOps, AWS CodePipeline