Licht Tech Limited, a Competency Enrichment and Skills Development Company is committed to empowering individuals and organisations with capacity building tools designed to optimize their personal, professional and corporate effectiveness. Our objective is to assist organisations achieve exceptional superior performance from investments in their human capital by providing highly stimulating and interactive forum for finding practical solutions to organizational human capital challenges.
Job Description
- We are seeking a skilled Data Scientist to join our team. The ideal candidate will have a strong background in data analysis, statistical modeling, and machine learning.
- You will be responsible for extracting insights from large datasets and developing data-driven solutions to support business decisions.
Key Responsibilities
- Data Analysis: Analyze large datasets to identify patterns, trends, and insights.
- Model Development: Develop and implement statistical models and machine learning algorithms to solve business problems.
- Data Cleaning: Perform data cleaning and preprocessing to ensure the accuracy and quality of data.
- Visualization: Create visualizations and dashboards to communicate findings and insights effectively to stakeholders.
- Collaboration: Work closely with cross-functional teams, including engineering, product management, and marketing, to identify data-related opportunities and deliver actionable insights.
- Reporting: Prepare comprehensive reports and presentations to share findings and recommendations with stakeholders.
- Research: Stay up-to-date with the latest developments in data science and machine learning and apply them to improve existing processes and models.
- Automation: Develop automated data pipelines and workflows to streamline data processing and analysis.
- Model Development: Design, develop, and implement machine learning models to solve complex business problems.
- Algorithm Selection: Select appropriate machine learning techniques for various applications, including supervised and unsupervised learning.
- Data Preparation: Clean, preprocess, and transform data to make it suitable for machine learning models.
- Evaluation: Evaluate the performance of machine learning models using appropriate metrics and techniques.
- Deployment: Deploy machine learning models into production and ensure they perform effectively over time.
- Research: Stay current with the latest advancements in machine learning and artificial intelligence and integrate new techniques into existing projects.
- Collaboration: Work closely with data scientists, engineers, and other stakeholders to integrate machine learning solutions into business processes.
- Optimization: Continuously improve and optimize machine learning models for better accuracy and efficiency.
- Documentation: Document machine learning processes, models, and experiments for future reference and reproducibility.
Qualifications
- Education: Bachelor's Degree in Data Science / Statistics / Computer Science, or a related field. A Master’s degree or Ph.D. is preferred.
- Experience: Minimum of 4 years of experience in data science or a related field.
- Technical Skills: Proficiency in programming languages such as Python, R, and SQL. Experience with data visualization tools like Tableau or Power BI.
- Tools: Familiarity with machine learning libraries and frameworks (e.g., scikit-learn, TensorFlow, PyTorch).
- Certifications: Relevant certifications in data science or machine learning are a plus.
- Experience with machine learning frameworks like TensorFlow, Keras, PyTorch, and scikit-learn.
- Tools: Familiarity with big data technologies (e.g., Hadoop, Spark) and cloud platforms (e.g., AWS, Google Cloud, Azure).
Skills and Competencies:
- Strong mathematical and statistical skills.
- Excellent problem-solving and analytical abilities.
- Ability to translate business requirements into technical solutions.
- Strong written and verbal communication skills.
- Attention to detail and ability to work independently.
- Experience with model evaluation and performance tuning.
Method of Application
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