Data Scientist

Primary Skills (Must have skills):

• Python: Proficiency in Python for data analysis, machine learning, and statistical modeling.
• Scala: Experience with Scala for big data processing and functional programming.
• SQL: Strong SQL skills for database querying and data manipulation.
• Data Modeling: Expertise in designing and implementing data models to support analytics and reporting.

Secondary Skills (Good to have skills):

• TensorFlow: Knowledge of TensorFlow for building and deploying machine learning models.
• PyTorch: Experience with PyTorch for deep learning applications.
• Denodo: Familiarity with Denodo for data virtualization and integration.

Experience Level:

• 10+ years of experience in data science, machine learning, and related fields.

Technical Implementation and Experience:

• Python: Implemented various machine learning algorithms, performed data cleaning and preprocessing, and developed data visualization dashboards using libraries like Pandas, NumPy, Scikit-learn, and Matplotlib.
• Scala: Developed and optimized big data processing pipelines using Apache Spark and Scala, ensuring efficient data handling and transformation.
• SQL: Designed and maintained complex SQL queries for data extraction, transformation, and loading (ETL) processes. Created and managed relational databases to support data-driven decision-making.
• Data Modeling: Created logical and physical data models, defined data relationships, and ensured data integrity and consistency across different systems.
• TensorFlow and PyTorch: Built and trained deep learning models for various applications such as image recognition, natural language processing, and predictive analytics. Deployed models into production environments and monitored their performance.
• Denodo: Utilized Denodo for data integration, creating virtual data layers to provide a unified view of data from multiple sources. Improved data accessibility and reduced data redundancy.

Detailed Responsibilities:

1. Data Collection and Cleaning:
• Collect data from various sources, including internal databases, web scraping, and third-party APIs.
• Clean and preprocess the data to remove errors and ensure its quality.
2. Data Analysis and Modeling:
• Analyze data using statistical and machine learning techniques.
• Develop predictive models and machine-learning algorithms.
• Conduct advanced data analysis and complex designs algorithm.
3. Visualization and Reporting:
• Create visualizations to present data findings effectively.
• Prepare reports and dashboards to help business stakeholders make informed decisions.
4. Collaboration and Communication:
• Work closely with different teams (e.g., IT, Marketing, Operations) to identify opportunities for leveraging company data.
• Present findings to stakeholders and provide recommendations based on data analysis.
5. Innovation and Strategy:
• Stay updated with the latest technology trends and advancements in data science.
• Explore new tools and techniques to improve existing data analytics processes.
• Assist in setting data-related strategic directions for the business.

• Analytical Skills: Strong mathematical and statistical analysis skills.
• Communication Skills: Excellent verbal and written communication skills.
• Problem-solving: Ability to approach complex challenges systematically and deliver data-driven insights.