Summary
Date Posted: Friday, June 28 2024, Base Salary: Not Disclosed

JOB DETAILS:
Job Purpose
Responsible for combining expertise in statistical analysis, programming, and domain knowledge to extract valuable insights and solve complex problems using data-driven approaches.

Principle Responsibilities
• Data Exploration and Preparation: Responsible for understanding the available data and performing exploratory data analysis (EDA). Assess data quality, handle missing values, outliers, and transform raw data into a format suitable for analysis.
• Statistical Analysis and Modelling: Employing statistical techniques to identify patterns, relationships, and trends within the data. Build predictive and prescriptive models, such as regression, classification, clustering, time series, and optimization models, to make informed decisions and generate actionable insights.
• Machine Learning and AI: Applying machine learning algorithms and artificial intelligence techniques to train models that can automatically learn from data, make predictions, and classify information. Select appropriate algorithms, perform model training, optimization, and evaluation to ensure accurate results.
• Feature Engineering and Selection: Engineer relevant features from the available data, which enhance model performance. Identify the most informative features and employ techniques like dimensionality reduction to eliminate irrelevant or redundant variables.
• Data Visualization and Communication: Create visual representations of data, such as graphs, charts, and interactive dashboards, to communicate findings effectively to stakeholders. Explain complex technical concepts in a clear and concise manner to facilitate decision-making.
• Collaboration and Domain Expertise: Collaborate with cross-functional teams, including business analysts, engineers, and domain experts, to understand the context and requirements of the analytics projects. Incorporate domain knowledge into the analysis and ensure that the results align with business goals.
• Model Deployment and Monitoring: Deploy models into production systems, integrating them with existing software or infrastructure. Monitor model performance, track key metrics, and implement strategies to retrain or update models when necessary to maintain accuracy and relevance.
• Data Privacy and Ethical Considerations: Adhere to ethical guidelines and ensure compliance with data privacy regulations. Handle sensitive information responsibly and implement safeguards to protect data privacy and security.
• Continuous Learning and Innovation: Stay up to date with the latest advancements in data science, machine learning, and analytics. Explore new techniques, tools, and methodologies to improve skills and contribute to the innovation and growth of the advanced analytics unit.

Qualifications Required
• A bachelor’s degree in a relevant field such data science, statistics, machine learning, computer science, mathematics, or a related field.
• Proficiency in programming languages like Python or R
• Familiarity with popular data science tools and libraries such as Jupyter Notebook, Pandas, NumPy, scikit-learn, TensorFlow, or PyTorch
• Familiarity with libraries like Matplotlib, ggplot, or Tableau
• Experienced using Azure ML, AWS, H2O, and other cloud-based solutions.
• Strong knowledge of statistics, probability theory, and mathematical concepts is crucial.
• Skills in hypothesis testing, regression analysis, experimental design, and statistical modelling.
• Proficiency in programming languages such as Python or R. Additionally, knowledge of tools like Jupyter Notebook, Pandas, NumPy, and scikit-learn is beneficial.
• Machine Learning and AI.
• Data Wrangling and Visualization
• Domain Knowledge
• Problem-Solving and Critical Thinking
• Communication and Storytelling
• Big Data and Distributed Computing
• Experimental Design and A/B Testing



Job Experience: No Requirements

Work Hours: 8

Experience in Months:

Level of Education: Bachelor Degree

Job application procedure

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