Data Scientist
Employment Type: Full-time (on-site)
Number of vacancy: 3
Job location: Dhaka, Bangladesh
Job Context:
MIAKI is an organization with skillful resources that are relentlessly working to make a difference in the spectrum of Tech & Digital Services. As a Multi-Country Service Provider, we specialize in developing customized software and mobile applications and providing outsourcing services for skilled resources in both local and international markets. Our expertise also extends to serving as a Managed Services Partner in the digital space and fostering the creation of companies and innovative ideas.
MIAKI believes in making a difference. We stand for value for money, quality, innovation, and a sense of competitive challenge. We strive to achieve this by empowering our employees to continually deliver customer experience.
We are looking for hiring passionate experienced Data Scientist for various projects with the below technology
Role and Responsibilities:
- Data Collection and Cleaning: Collecting, cleaning, and preprocessing large datasets to ensure data quality and suitability for analysis.
- Data Exploration: Exploring and visualizing data to identify patterns, trends, and anomalies.
- Statistical Analysis: Applying statistical methods to draw meaningful conclusions from data, including hypothesis testing and regression analysis.
- Machine Learning: Developing and implementing machine learning models for predictive and prescriptive analytics.
- Feature Engineering: Creating and selecting relevant features or variables to improve model performance.
- Model Evaluation: Assessing the performance of machine learning models using various metrics and techniques like cross-validation.
- Model Deployment: Taking models from development to production, ensuring they are integrated into business processes.
- Data Visualization: Creating informative and compelling visualizations to communicate findings effectively.
- Data Storytelling: Translating complex data insights into understandable and actionable recommendations for non-technical stakeholders.
- Domain Knowledge: Gaining expertise in the specific industry or domain to understand the context of data and its implications better.
Must Have Skills:
- Data Collection and Cleaning: Collecting, cleaning, and preprocessing large datasets to ensure data quality and suitability for analysis.
- Data Exploration: Exploring and visualizing data to identify patterns, trends, and anomalies.
- Statistical Analysis: Applying statistical methods to draw meaningful conclusions from data, including hypothesis testing and regression analysis.
- Machine Learning: Developing and implementing machine learning models for predictive and prescriptive analytics.
- Feature Engineering: Creating and selecting relevant features or variables to improve model performance.
- Model Evaluation: Assessing the performance of machine learning models using various metrics and techniques like cross-validation.
- Model Deployment: Taking models from development to production, ensuring they are integrated into business processes.
- Data Visualization: Creating informative and compelling visualizations to communicate findings effectively.
- Data Storytelling: Translating complex data insights into understandable and actionable recommendations for non-technical stakeholders.
- Domain Knowledge: Gaining expertise in the specific industry or domain to understand the context of data and its implications better.
Preferred Skills:
- Big Data Technologies: Familiarity with big data tools and frameworks like Hadoop and Spark.
- Deep Learning: Understanding of deep learning algorithms and frameworks like Tensor Flow or PyTorch.
- Cloud Computing: Experience with cloud platforms like AWS, Azure, or Google Cloud for scalable data processing and storage.
- Natural Language Processing (NLP): Knowledge of NLP techniques for text analysis and sentiment analysis.
- A/B Testing: Experience in designing and conducting A/B tests to evaluate the impact of changes or interventions.
- Data Engineering: Basic knowledge of data engineering concepts to work effectively with data pipelines and ETL processes.
- Business Acumen: Understanding of business goals and the ability to align data science projects with organizational objectives.
- Collaboration: Collaboration and teamwork skills to work effectively with cross-functional teams.
- Version Control: Familiarity with version control systems like Git for code management.
- Continuous Learning: A willingness to stay updated with the latest trends and technologies in data science.
Preferred Qualifications:
- The candidate should have 5 years of hands on working experience in specific technologies
- B.Sc. Engineering in Computer Science and bachelor's degree in data science, machine learning, artificial intelligence, or a related quantitative discipline
- Familiarity with data science tools and libraries, such as Jupyter Notebook, Pandas, NumPy, and Scikit-learn.
- Version control systems like Git for code management.
- Experience with cloud platforms like AWS, Azure, or Google Cloud can be advantageous
- Enjoy working on challenging solutions and systems
- A can do attitude
Salary: Competitive and negotiable salary range, based on market analysis and hands on working experience
Other benefits:
- 2 Festival bonus
- Mobile Allowance
- 2 days weekly holiday
- Medical Insurance coverage along with Spouse & Kids
- Provident Fund and Gratuity
- Opportunities for professional growth and development.
- Collaborative and inclusive work culture.
- Opportunity to work on exciting and innovative projects
Position interviewing process:
- Introductory discussion with HR
- Online interview with the technical team
- Final interview with the Project Owner
Application Instructions:
Interested candidates are encouraged to submit the resume by mentioning the post name of the job in the email subject line to [email protected]