Data Scientist at Matrix Global

, , Portugal -

Full Time

Start Date

Immediate

Expiry Date

12 May, 26

Salary

0.0

Posted On

11 Feb, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, NumPy, Pandas, Scikit-learn, Machine Learning, Distributed Computing, PySpark, Data Cleaning, Feature Engineering, Data Pipelines, Communication, Analytical

Industry

Financial Services

Description

Matrix IFS is looking for a Data Scientist, the job involves working with client data to develop and implement fraud detection models, which includes data cleaning, feature engineering, and model tuning. Responsibilities also include collaborating with clients and internal teams, communicating findings, and contributing to product development by creating scalable data pipelines and improving processes. Key skills required are proficiency in Python (including libraries like NumPy, Pandas, and scikit-learn), machine learning, distributed computing (like PySpark), and strong communication abilities. Core responsibilities Data analysis and modeling: Understand client data, perform data cleaning and preprocessing, and develop and tune machine learning models. Collaboration: Work with internal teams (Product, Research) and external stakeholders (clients) to understand business objectives and ensure client success. Technical leadership: Provide technical guidance, drive project execution, and contribute to building scalable data pipelines and prototypes. Product development: Help improve the product by contributing to solution development, keeping up with fraud prevention trends, and creating generalized product enhancements. Communication: Clearly communicate complex findings and strategies to both technical and non-technical audiences. Key skills and qualifications Programming: Strong proficiency in Python, including scientific stack libraries (NumPy, Pandas, SciPy, scikit-learn) and experience with distributed computing tools like PySpark. Machine Learning: Expertise in machine learning concepts and algorithms. Data Manipulation: Experience with data cleaning, feature engineering, and building scalable data pipelines (e.g., with Airflow and Trino). Communication: Excellent verbal and written communication skills to explain complex ideas to various stakeholders. Analytical: Strong analytical and problem-solving skills to tackle complex financial crime challenges. Education: A degree in a quantitative field like Computer Science, Statistics, Applied Mathematics, or a related area is often required. Why Matrix Matrix is a global, dynamic, fast-growing technical consultancy leading technology services company with 13000 employees worldwide. Since its foundation in 2001, Matrix has made more travelers and acquisitions and has executed some of the largest, most significant. The company specializes in implementing and developing leading technologies, software solutions, and products. It provides its customers with infrastructure and consulting services, IT outsourcing, offshore, training and assimilation, and Ves as representatives for the world's leading software vendors. With vast experience in private and public sectors, ranging from Finance, Telecom, Health, Hi-Tech, Education, Defense, and Secu city, Matrix's customer base includes guest organizations in Israel and a steadily growing client base worldwide. We are comprised of talented, creative, and dedicated individuals passionate about delivering innovative solutions to the market. We source and foster the best talent and recognize that all employee's contributions are integral to our company's future. Matrix- success is based on a challenging work environment, competitive compensation and benefits, and rewarding career opportunities. We encourage a diverse work environment of sharing, learning, and ceding together. Come and join the winning team! You'll be challenged and have fun in a highly respected organization. To Learn More, Visit Matrix -ifs. Com, Requirements null

Responsibilities

The Data Scientist will analyze client data to develop and implement fraud detection models, including data cleaning and model tuning. Responsibilities also involve collaborating with clients and internal teams, communicating findings, and contributing to product development.