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Data scientist – Data Engineer
A highly competent data scientist with seven years of experience developing a wide range of models like Credit Scoring, Predictive Segmentation & RFM, and Customer 360 etc. Ability to use (data) statistics and machine learning for finding complex data patterns that drive meaningful impact on the business.
I am looking for the opportunity to build a challenging career and apply my skills in an innovative and simplify process. I enjoy working in a team and communicating data-driven results.
- PowerBI & Tableau
- Predictive RFM model (Nepal-Ncell): predictive segmentation model based on 12 Million subscriber’s historical behaviour (recency, frequency and monetary), used in campaign management.
- Gross Adds Optimization (Bangladesh-Robi): Contributed to the project by building timeseries models to predict Add back’s ARPU and subscriber count at state level.
- Next Best Action – NBA (Celcom-Malaysia): Contributed in development of Celcom NBA which helps personalize the offers using subscriber’s historical purchase data.
- Data management and pipelines (Malaysia-Apigate): Synchronizing more that 1T of data between AWS and GCP datalake, design and implement more than 80 daily, hourly and near real-time pipelines.
- Stretch model (Malaysia-Apigate): Help business team to identify segment of customer that can be targeted as recoverable revenue by processing up to 1 billion records of transactional data.
- Customer journey (Malaysia-Apigate): end to end segmentation model of transactions that help business team apply data driven decisions on products and content providers.
Big Data Analyst
- Credit Scoring (Sri Lanka-Dialog): Developed different models for fintech microlending and cross selling, based on telco subscriber’s products usage and transactional behaviour and retail revenue and transactional history.
- DRIP – Predictive Customer Segmentation (Nepal-Ncell): Developed predictive models for customer ARPU and data usage segmentations based on 10 million customer’s historical data. Output of predictive model is used in campaign management which produced incremental revenue.
- As team member to perform research and development in field of cloud computing and edge computing which led to creation of a platform called Mi-Focus. This platform was used by ministry of Health to deploy Teleprimary Care – Oral Health Clinical Information System (TPC-OHCIS) at clinics in Malaysia.
Master of Computer Science
Bachelor of Computer Science & Networking
Stock Price Prediction
Use the Sentiment Analysis Algorithms to understand the stock sentiments.
Project: Twitter Sentiment Analysis
Tools: NLTK, Python
Algorithms: Sentiment Analysis
Stock Recommender Systems
Recommender systems have become the most popular feature of the stock market. We have developed a artificial intelegint stock recommender tool using the Deep Neural Networks and classification algorithms
Tools: Python, sklearn
Algorithms: Deep Neural Networks, classification algorithms
Customer Support System
Customer support has become a challenging job for every business. We build a “customer support system” to address and support the needs of the customers.
Tools: OpenCV, Python
Algorithms: Convolution Neural Network and other facial detection algorithms
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What Should Be Included In A Data scientist Resume?
Writing a data scientist resume can be a difficult task because it requires knowledge of the industry and an understanding of what potential employers are looking for in a qualified applicant. A data scientist resume should include a summary of qualifications and relevant experience, a portfolio of data-oriented projects and certifications, as well as education and training.
Your summary should be tailored to the specific job you are applying for and highlight your qualifications and experience. It should also include key skills and qualifications that are important to the position. This section should be short and to the point, demonstrating your knowledge of the industry and the particular job you are applying for.
Your portfolio should include data-oriented projects that you have worked on, such as data cleaning, analysis, visualization, and modeling. Include examples of code, reports and any other materials related to the project. This helps the employer understand your experience and capabilities. You may also want to include certifications you have achieved related to data science and analytics, such as the Certified Data Scientist (CDS) or a related certification.
Your education and training should also be included in your data scientist resume. Include any degrees or certificates related to data science and analytics, as well as any research projects, internships, or other programs you have participated in. This will demonstrate to employers that you have the necessary knowledge and skills to be a successful data scientist.
When writing your data scientist resume, it is important to remember that employers are looking for qualified applicants who can effectively analyze and interpret data to help them make better decisions. Make sure to include all relevant information that demonstrates your qualifications and experience, and make sure that your resume is concise and well-written.
What Skills Should I Put On My Resume For Data scientist?
Data scientists are one of the most in-demand professions right now, and it is important for job seekers to make sure their resume is up to date and showcases the skills needed for the role. When applying for any data scientist role, it is critical to highlight the skills on your resume that make you the ideal candidate. Fortunately, there are a few key skills that data scientists should always consider including on their resume.
First and foremost, data scientists should be sure to include any technical skills they possess. This includes programming languages such as Python, R, and SQL, as well as any other software that could be beneficial in the role, such as machine learning platforms. Additionally, data scientists should be sure to include any experience they have with data analysis and manipulation, such as knowledge of data visualization techniques, data cleaning, and data processing.
Other skills that data scientists should include on their resume are communication and problem-solving skills. Data scientists must be able to communicate complex data concepts and findings to non-technical audiences, so having good written and verbal communication skills is essential. Additionally, data scientists must be able to use their analytical skills to identify patterns in data and develop solutions to complex problems.
Finally, employers also want to see that data scientists have the ability to work in a collaborative environment, so it is important to showcase any experience you may have working with teams. This can be done by highlighting any projects you have worked on, as well as any collaborative methods you have employed when working with data.
By highlighting your technical, communication, problem-solving, and teamwork skills, you can be sure that your resume will stand out from the competition and make you a more desirable candidate for any data scientist role.
What Is The Job Description Of The Data scientist?
.The Data Scientist is a profession that involves the use of advanced mathematics, computer science and data analysis to interpret data and solve complex problems. It is a field of study that combines mathematics, statistics, data science, and computer science. Data Scientists use a wide range of techniques to extract knowledge and insights from structured, semi-structured, and unstructured data. They are responsible for developing and implementing algorithms, creating predictive models, and using data to inform decisions.
Data Scientists need to have a deep understanding of the data they work with. This includes its sources, structure, and how to interpret the data. They need to be experts in their field, which requires a combination of science, mathematics, and computer programming. Data Scientists also need to understand how to manipulate data and use it to create models and insights.
Data Scientists are also responsible for understanding the needs of their organization and how to design and implement data-driven solutions. They must be able to communicate their findings to stakeholders and business decision makers. Furthermore, they need to be knowledgeable about the latest technologies and have the ability to apply them to their work.
Data Scientists are highly sought after in today’s job market. Companies are looking for professionals with the skills to analyze data to create meaningful insights and solve complex problems. To land a job as a Data Scientist, you need to make sure your resume stands out. This guide will give you the tips and advice you need to write a great Data Scientist resume.
What Is A Good Objective For A Data scientist Resume?
When writing a data scientist resume, it’s important to have a clear and concise objective statement. The objective statement should clearly state what you hope to achieve during your tenure as a data scientist. A good objective will emphasize your skills, experiences, and qualifications that are most relevant to the job.
When writing an objective for your data scientist resume, it’s important to keep it concise and to the point. Your objective should focus on the skills and experiences that are most pertinent to the job you are applying for. It should also provide a brief summary of your professional background and success.
It’s also important to include any certifications or specializations that you hold. This will show potential employers that you have the knowledge and experience necessary to succeed in the position.
In addition to the objective statement, it’s also important to include a list of your qualifications and experiences. This will help to demonstrate how qualified you are for the position and how you can contribute to the organization’s success.
Finally, make sure to include a summary of your achievements as it pertains to data science. This will show employers that you have the necessary skills and experience needed to make a positive impact.
By following these tips, you can create an effective objective statement for your data scientist resume that will help you stand out from the competition and get the job you want.
What Are The Career Prospects In The Data scientist?
Data science is quickly becoming one of the most sought-after skills in the job market. Data scientists have the potential to earn impressive salaries, have a wide variety of job opportunities and enjoy working in a highly creative field. With the right qualifications and experience, data scientists can make a significant impact in the corporate world.
Data scientists play an integral role in the success of any business. They are experts in the analysis, visualization and presentation of data, and are responsible for driving the decisions and strategies of the organization. Additionally, data scientists are often tasked with creating predictive models, building and maintaining databases, and developing data-driven solutions for businesses.
Data scientists are often hired by organizations in the banking, media, retail, manufacturing and healthcare industries. In addition to traditional industries, data scientists are increasingly being used by technology companies to develop innovative solutions. The demand for data scientists is expected to grow as businesses become increasingly reliant on data-driven decision making.
Data scientists must possess a range of skills, including technical expertise in programming languages such as Python, R, and SQL. It is also important for data scientists to have strong communication skills, as they will often be required to present their findings to senior leaders. Finally, data scientists must remain up to date on advances in the field, as data science is constantly evolving.
Data scientists have the potential to earn impressive salaries and have a wide variety of job opportunities. With the right qualifications and experience, data scientists can make a significant impact in the corporate world.
Key Takeaways for an Data scientist resume
A Data scientist resume should be carefully crafted to make the most of a job seeker’s qualifications. Writing an effective resume requires a few key considerations. These key takeaways include understanding the job requirements, highlighting relevant experience and skills, emphasizing soft skills, and showcasing accomplishments.
When writing a Data scientist resume, it is important to fully understand the job requirements. Before writing, job seekers should take the time to research the role they are applying for. This research should include reading the job description and researching the company. Knowing the type of work that is expected can help job seekers tailor their resume to the job, highlighting the necessary skills and experience.
The experience and skills a job seeker has should be highlighted prominently on their Data scientist resume. Relevant experience should be the cornerstone of the resume, as employers will be looking for expertise in data science, statistics, programming languages, and other related technologies. It is also important to emphasize soft skills, such as communication, problem-solving, and creativity.
Finally, job seekers should showcase their accomplishments on their Data scientist resume. Accomplishments can include awards and prizes, as well as any relevant skills acquired from previous positions. Accomplishments will not only show employers that the job seeker is experienced and knowledgeable, but they will also demonstrate the job seeker’s ability to be successful in the role.
By understanding the job requirements, highlighting relevant experience and skills, emphasizing soft skills, and showcasing accomplishments, job seekers can create an effective Data scientist resume. This resume will show potential employers that the job seeker is well-suited for the role and can make a positive contribution to their team.
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