Senior Data Analyst

3 weeks ago


Kuala Lumpur, Malaysia Capgemini Full time

Short Description

We are currently seeking a talented and experienced Senior Data Scientist to join our dynamic team. This is an exciting opportunity to work on cutting-edge projects and contribute to our company's success.

The ideal candidate should have a strong background in statistical analysis, machine learning algorithms, and programming skills, along with experience in handling large-scale data sets. The role will involve collaborating with cross-functional teams to develop and deploy data-driven solutions that address business challenges and opportunities.

Qualifications

Master's or Ph.D. degree in Computer Science, Statistics, Mathematics, or a related field. Proven work experience as a Senior Data Scientist or similar role. Strong analytical skills, critical thinking, and problem-solving abilities. Proficiency in Python (including XGBoost, LightGBM, NLTK, Spacy, Pyspark, Tensorflow, networkx), Dataiku, and SAS. Business acumen in the banking industry. Leadership skills and experience in stakeholder management. Excellent communication and presentation skills.

Job Responsibilities

Lead and mentor a team of data scientists in developing and implementing advanced analytics solutions. Collaborate with business stakeholders to understand their requirements and translate them into data science projects. Design and implement machine learning models and algorithms to analyze large-scale datasets, utilizing Python (including XGBoost, LightGBM, NLTK, Spacy, Pyspark, Tensorflow, networkx), Dataiku, and SAS. Apply business acumen, particularly in the banking domain, to develop data-driven insights and solutions. Demonstrate leadership skills and stakeholder management to effectively communicate findings and drive decision-making. Conduct statistical analysis and hypothesis testing to derive actionable insights from data. Develop data pipelines and workflows for data processing and model deployment. Stay updated with the latest trends and best practices in data science and machine learning.

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