Required Skills

· Collect and gather data from various sources, including internal databases, external websites, and surveys.

· Clean and prepare data for analysis by removing errors, duplicates, and inconsistencies.

· Transform and manipulate data using statistical techniques and data visualization tools.

· Identify trends, patterns, and relationships in data to uncover insights.

· Communicate findings and insights to stakeholders in a clear and concise manner.

· Develop data-driven recommendations and strategies to improve business operations.

· Stay up-to-date on the latest data analysis tools and techniques.

· Proficiency in data visualization tools like Tableau, Power BI, or Qlik Sense.

· Ability to communicate complex data findings to both technical and non-technical audiences.

Data Analyst

Data Scientist

Required Skills

· Python and PySpark are required

· Proficiency in data science modeling - AI, Machine Learning, Deep Learning, Decision Trees, Random Forest, Neural Networks

· Strong background in machine learning using unsupervised and supervised methods.

· Deep knowledge of fundamentals of machine learning, data mining and statistical predictive modeling, and extensive experience applying these methods to real world problems

· Strong skills in software prototyping and engineering with expertise in applicable programming and analytics languages

· Proven experience of using Python Machine Learning & Data Pre-processing Libraries.

· Ability to transform concepts into practical solutions. Experience with data science/ML techniques

· Machine Learning experience -- Successful implementation of ML solutions

Data Engineer

Required Skills

· Design and develop data pipelines to collect, store, and process data from various sources.

· Implement data warehousing and data mart solutions to store and analyze data.

· Develop and maintain data quality checks to ensure the accuracy and consistency of data.

· Build and maintain data infrastructure using cloud-based platforms like AWS, Azure, or GCP.

· Automate tasks using scripting languages like Python, Bash, or SQL.

· Collaborate with data scientists and analysts to ensure the data infrastructure supports their needs.

· Experience with cloud-based data platforms such as AWS, Azure, or GCP.

· Knowledge of data warehouse and data mart architectures.

· Understanding of data quality principles and practices.

· A passion for data and a desire to build data pipelines that is efficient and scalable.