Role Proficiency:

Independently develop data-driven solutions to difficult business challenges by utilize analytical statistical and programming skills to collect analyze and interpret large data sets under supervision.

Outcomes:

      Work with stakeholders throughout the organization to identify opportunities for leveraging data from our customers to make models that can generate business insights       Create new experimental frameworks or build automated tools to collect data       Correlate similar data sets to find actionable results       Build predictive models and machine learning algorithms to analyse large amounts of information to discover trends and patterns.       Mine and analyse data from company databases to drive optimization and improvement of product development marketing techniques business strategies etc       Develop processes and tools to monitor and analyse model performance and data accuracy.       Develop Data Visualization and illustrations on given business problem       Use predictive modelling to increase and optimize customer experiences and other business outcomes.       Coordinate with different functional teams to implement models and monitor outcomes. Set FAST goals and provide feedback on FAST goals of reportees

Measures of Outcomes:

      Number of business processes changed due to vital analysis.       Number of Business Intelligent Dashboards developed       Number of productivity standards defined for project       Number of Prediction and Modelling models used       Number of new approaches applied to understand the business trends       Quality of data visualization done to help non-technical stakeholders comprehend easily. Number of mandatory trainings completed

Outputs Expected:

Statistical Techniques:

Apply statistical techniques like regression
properties of distributions
statistical tests
etc. to analyse data.

Machine Learning Techniques:

Apply machine learning techniques like clustering
decision tree learning
artificial neural networks
etc. to streamline data analysis.

Creating advanced algorithms:

Create advanced algorithms and statistics using regression
simulation
scenario analysis
modelling
etc.

Data Visualization:

Visualize and present data for stakeholders using: Periscope
Business Objects
D3
ggplot
etc.

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:

Creating efficient and reusable code meant for the improvement
manipulation
and analysis of data.

Version Control:

Manages project codebase through version control tools e.g. git
bitbucket etc.

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

Create Reports:

Creates reports depicting the trends and behaviours from the analysed data Training end users on new reports and dashboards.

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

Skill Examples:

      Excellent pattern recognition and predictive modelling skills       Extensive background in data mining and statistical analysis       Expertise in machine learning techniques and creating algorithms.       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 numbers trends and data and come to new conclusions based on the findings.       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 and have strong interpersonal skills to work in collaborative environment Proactively ask for and offer help

Knowledge Examples:

Knowledge Examples

      Programming languages – Java/ Python/ R.       Web Services - Redshift S3 Spark DigitalOcean etc.       Statistical and data mining techniques: GLM/Regression Random Forest Boosting Trees text mining social network analysis etc.       Google Analytics Site Catalyst Coremetrics Adwords Crimson Hexagon Facebook Insights etc.       Computing Tools - Map/Reduce Hadoop Hive Spark Gurobi MySQL etc.       Database languages such as SQL NoSQL       Analytical tools and languages such as SAS & Mahout.       Practical experience with ETL data processing etc.       Proficiency in MATLAB.   Data visualization software such as Tableau or Qlik.   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 about sub domain where problem is solved   Proficient in at least 1 version control tool like git bitbucket Have experience working with project management tool like Jira

Additional Comments:

Must have -Statistical Concepts, SQL, Machine Learning (Regression and Classification), Deep Learning (ANN, RNN, CNN), Advanced NLP, Computer Vision, Gen AI/LLM (Prompt Engineering, RAG, Fine Tuning), AWS Sagemaker/Azure ML/Google Vertex AI, Basic implementation experience of Docker, Kubernetes, kubeflow, MLOps, Python (numpy, panda, sklearn, streamlit, matplotlib, seaborn)