Data Scientist

Job Description

Job Title: Data Scientist

Job Location: Dubai

Job Description:

Key Responsibilities:

• Design and implement advanced analytical insights using applicable data science models utilizing Cloudera CML platform.
• Support the Data Analyst with applicable study and identify the most effective ways of using customer data, such as calls, video, social media, and other sources, to analyse customer needs and automatically solve problems.
• Develop data analytics and machine learning models to extract insights from customer data, such as sentiment analysis, topic modelling, and predictive modelling. 4. Use Case Preparation: Coordination of Data Scientists with Data Analyst and business stake holders in identification of use cases.
• Data Collection:
• Identify and gather relevant data from various sources in coordination with Data Engineers. Data Analysts will assist in this process by identifying key data sources and helping to extract and clean the data.
• Ensure data quality working with the Data Quality Lead for the extracted data set to be scanned and profiled with Informatica Data Quality tool to benchmark the data quality and to decide whether the data requires to be synthetic or not.
• Data Cleaning: Ensure data is clean and standardized working with the Data Quality Lead to ensure that its accurate and suitable for analysis. Data Analysts can assist in this process by identifying inconsistencies or errors in the data and helping to clean it. Exploratory Data Analysis: Data Scientists are responsible for analysing the data to identify patterns and trends. This includes using statistical methods and machine learning algorithms.
• Data Analysts can assist in this process by providing insights and feedback on the analysis and helping to interpret the results.
• Analysis to be in detailed related to Univariate, BiVariate analysis.
• Business Requirement Specification: Data Scientist and Data Analyst to document the BRS for the use case being implemented and obtain the sign off from the business
• Model Building: Data Scientists must build and train machine learning modelsto make predictions or classifications based on the data. This includes selecting the appropriate algorithms and tuning the model parameters. Data Analysts can assist in this process by helping to test and validate the models and providing feedback on their performance. Results to be showcased to business with various models with statistical evidence on pros and cons of the models with the expected accuracy.
• Deployment: Data Scientists are responsible for deploying the models into production and integrating them with other systems. Data Analysts can assist in this process by helping to monitor the performance of the models and providing insights on any issues or anomalies. All the deployments will follow DM Release policy.
• Retraining: Data Scientists on regular basis to retrain the model and make sure the accuracy is consistent across the models
  
  

• A trained model based on the BRS.
• Support Business Intelligence team in integrating the model with the BI Data Product (Dashboards, Reports)
• Develop Model Deployment Pipelines
• Model Prototypes or Proof-of-Concepts (PoCs)
  
  

• Strong Statistical Background: Data Scientists should have a strong foundation in statistical concepts, including probability theory, hypothesis testing, and regression analysis.
• Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
• Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.
• Machine Learning: Data Scientists should have experience with machine learning algorithms, including both supervised and unsupervised learning methods, and have a good understanding of how to select the appropriate algorithm for a given problem.
• Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modelling, clustering, decision trees, neural networks, etc.
• Experience with utilizing ready cloud ML models.
• Data Visualization: Data Scientists should be able to effectively communicate their findings through data visualization tools like Tableau, Power BI, or Python libraries like Matplotlib or Seaborn.
• Programming Skills: Data Scientists should be proficient in programming languages like Python or R, as well as have a good understanding of SQL, Oracle and database management. Experience using web services: Redshift, S3, Spark, Digital Ocean, etc.
• Experience analysing data from 3rd party providers: Google Analytics, Site Catalyst, Core metrics, AdWords, Crimson Hexagon, Facebook Insights, etc.
• Data Wrangling: Data Scientists should have strong skills in data cleaning, data pre- processing, and data wrangling to ensure that the data is in a suitable format for analysis.
• Big Data Technologies: Data Scientists should have experience with big data technologies like Hadoop, Spark, or NoSQL databases, as well as cloud-based technologies like Amazon Web Services (AWS) or Microsoft Azure.
• Communication and Collaboration: Data Scientists should be able to effectively communicate their findings to both technical and non-technical stakeholders, and work collaboratively with other team members, including Data Analysts, Engineers, and Data Analysts and Data Analysts
• Data Mining: Data Scientist to continuously look for opportunities to utilize data trends and patterns and mine for data. Analytical Skills
• Problem-Solving: Data Scientists should have strong problem-solving skills to be able to identify and solve complex data-related problems.
• Critical Thinking: Data Scientists should be able to analyse data and identify patterns, trends, and insights to make informed decisions.
• Experiment Design: Data Scientists should be able to design and run experiments to validate data-driven hypotheses.
• Performance Metrics: Data Scientists should be able to identify and define performance metrics to measure the effectiveness of machine learning models.
• Optimization: Data Scientists should be able to optimize algorithms to improve their accuracy, speed, and efficiency.
• Forecasting: Data Scientists should be able to build models to forecast future trends and identify potential risks and opportunities.