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Intermediate Data Analyst
4 months ago
As an Intermediate, your typical day would need:
The day usually starts with checking emails, messages, and any urgent notifications. The data scientist might plan their day, review the progress of ongoing projects, and prioritize tasks for the day.
To collaborate with cross-functional teams, including other data scientists, analysts, engineers, product managers, and stakeholders. They might participate in meetings to discuss project progress, data requirements, and future strategies.
To write and review code regularly. They might be involved in coding tasks related to data preprocessing, model development, or creating scripts for automating repetitive tasks.
To evaluate the performance of their machine learning models. They conduct validation tests and fine-tune the models to improve their accuracy and generalization capabilities.
> Technical Responsibilities
Data Analysis and Preprocessing
Machine Learning Model Development
Statistical Modeling
Data Visualization
Collaboration and Communication
Data Integration and Engineering
Model Deployment and Monitoring
Continuous Learning
> Management Responsibilities
Manage team
Decide team for the project
Manage Workload
Technical coaching and mentoring
Raising Hiring and recruitment needs
Mentor junior members of the team and participate in code reviews to help them grow inside the team.
Joint Responsibilities with Senior
Project specification document
Project Architecture
Weekly Project Meetings
You will be reporting to the Team Lead
Meaningfully contribute to agile ceremonies including grooming, planning, scrums, retrospectives.
Over time, we build out insights and knowledge of the industries that we work in.
About August
By leveraging user-centric thinking, design capabilities, new technologies and cloud solutions, we empower our clients and allow them to rethink how they connect with their customers across every platform, every device and every step of the customer journey. As part of an end-to-end delivery, our Technical team builds compelling digital experiences with a focus on channels such as the web and mobile.
As an Intermediate Data Science, you will have a:
Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or a related field.
Minimum of 2-3 years of hands-on experience in data science, machine learning, and statistical modeling.
Experience leading a team, working with others to solve problems, sharing knowledge and can help grow and mentor junior developers;
A passion for digital transformation and technology;
Experience in agile development environments, including ceremonies such as backlog refinement and sprint planning;
You can work independently as well as in a highly collaborative team-setting;
You balance logical problem solving with innovative thinking;
You like building positive client and peer relationships by developing an understanding of customer's business and project scope and can translate business requirements into technical requirements;
You can contribute positively to team culture, with a focus on inclusion and diversity;
Identifying potential opportunities for additional revenue generation;
Developing analytical and insightful recommendations to meet deliverable targets and complete project tasks within budget and to customer satisfaction;
Identifying, communicating and solving conflicts or issues to Team;
Assisting in the development of client proposals;
Strong written and verbal communication and presentation skills (English)
Requirements
Strong proficiency in programming languages such as Python or R for data manipulation and analysis.
In-depth knowledge of various machine learning algorithms, including supervised and unsupervised techniques.
Experience with data visualization libraries and tools, such as Matplotlib, Sea-born, or Tableau.
Solid understanding of statistical concepts and their application in data analysis and modeling.
Familiarity with big data frameworks like Hadoop and Spark is a plus.
Strong problem-solving skills and the ability to approach business challenges with a data-driven mindset.
Excellent communication skills to present findings and insights to technical and non-technical audiences.
Proven track record of delivering data-driven solutions that positively impact business outcomes.
Experience with deep learning frameworks, such as TensorFlow or PyTorch.
Knowledge of natural language processing (NLP) techniques and applications.
Benefits
- 5 Days Working- Technical Growth- Permanent Remote- Monthly Performance ReviewRequirementsTechnical Requirements - Data Science - Artificial Intelligence - Machine Learning - Deep Learning
