Data Science Consultant

About Us



Key Responsibilities:

• Data Analysis and Modelling:

  Collect, process, and analyse large datasets to extract actionable insights. Develop and implement statistical and machine learning models to solve complex business problems. 

• Algorithm Development:

  Design and develop algorithms for data mining, predictive modelling, and other data-driven applications. Continuously improve and optimize algorithms for better performance. 

• Visualization and Reporting:

  Create data visualizations and reports to effectively communicate insights and findings to stakeholders. Develop dashboards and interactive tools for data exploration. 

• Collaboration:

  Work closely with cross-functional teams, including data engineers, AI engineers, and product managers, to understand project requirements and deliver high-quality solutions. 

• Data Preparation:

  Perform data cleaning, transformation, and augmentation to ensure data quality and readiness for analysis. Implement ETL processes to streamline data workflows. 

• Machine Learning Implementation:

  Develop and deploy machine learning models to production environments. Monitor model performance and implement necessary updates and improvements. 

• Documentation and Reporting:

  Document the data analysis process, including data sources, methodologies, and results. Prepare and present reports on project progress and findings to stakeholders. 

• Ethical Data Practices:

  Ensure that data analysis and modelling adhere to ethical standards and guidelines, promoting fairness and minimizing bias. 

• Continuous Learning and Improvement:

  Stay updated with the latest advancements in data science and machine learning. Attend conferences, read research papers, and participate in professional development activities to continuously enhance skills and knowledge. 

• Problem-Solving and Innovation:

  Identify and solve complex problems using innovative data-driven solutions. Propose and implement creative ideas to leverage data for various applications and industries. 

• Testing and Validation:

  Conduct rigorous testing and validation of models and algorithms to ensure accuracy, reliability, and scalability. Implement A/B testing and other validation techniques to assess the real-world performance of data-driven solutions. 

• Feedback Incorporation:

  Collect and analyse feedback from users and stakeholders to improve data models and applications. Iterate on model development based on user needs and project requirements. 

Skills and Qualifications:

• Technical Skills:

   
• Proficiency in programming languages such as Python, R, and SQL. 
• Strong knowledge of machine learning frameworks and libraries, including TensorFlow, PyTorch, and Scikit-learn. 
• Experience with data visualization tools such as Tableau, Power BI, and Matplotlib. 
• Familiarity with big data technologies, including Hadoop and Spark. 
• Knowledge of data pre-processing, feature engineering, and ETL processes. 

Mathematics and Statistics:

• Strong foundation in linear algebra, calculus, and probability & statistics. 

Soft Skills:

• Excellent problem-solving and critical thinking skills. 
• Strong communication skills for explaining complex technical concepts to non-technical stakeholders. 
• Ability to work effectively in a team and collaborate with cross-functional teams. 
• Commitment to continuous learning and staying updated with industry advancements. 
• Creativity and innovation in developing data-driven solutions. 

Domain Knowledge:

• Understanding of the Financial Services industry and its specific challenges. 
• Awareness of ethical considerations in data science and machine learning. 

Overall and Relevant Experience:

• 3+ years overall IT experience with at least 2+ years of relevant experience in data science with Financial Services organisations. 
• Bachelor’s degree in computer science, Engineering, Statistics, Mathematics, or a related quantitative field. 

Why Join Us:

• Innovative Environment:

  Work on cutting-edge AI technologies and innovative projects. 

• International Exposure :

  To work with international clients/team 

• Collaborative Culture:

  Be part of a passionate and supportive team. 

• Professional Growth:

  Opportunities for continuous learning and development. 

• Impactful Work:

  Contribute to meaningful projects that drive innovation and make a difference. 

If you are excited about the prospect of working in a dynamic and forward-thinking company, we would love to hear from you!