Required Data Scientist
Your Mission:
As a Data Scientist, your mission will be to leverage your expertise in machine learning and generative AI to develop and implement models and Gen-AI tools that enhance our analytics, product development, and operational efficiency. You will play a crucial role in interpreting and analyzing complex data sets to provide actionable insights, drive strategic decision-making, and create cutting-edge innovative solutions that keep us at the forefront of the payment technology industry.
You will collaborate closely with data engineers, business analysts, and product teams to identify opportunities for leveraging company data in new ways. Through your work, you will enable us to harness the full potential of our data, ensuring that our strategies and products are informed by deep, data-driven insights.
Responsibilities:
Develop and implement advanced machine learning and generative AI models to solve complex business problems, enhance product offerings, and improve customer experiences.
Conduct data exploration and feature engineering to uncover hidden insights and identify opportunities for leveraging data across the business.
Collaborate with cross-functional teams to understand business needs and provide data-driven solutions and recommendations.
Stay abreast of the latest developments in machine learning and Gen-AI technologies, applying cutting-edge techniques and tools to drive innovation within.
Evaluate model performance and continuously iterate on and refine models to maintain their accuracy and relevance.
Communicate complex data concepts and the results of analyses clearly and effectively to stakeholders across the company.
Utilize big data technologies to process and analyze large datasets efficiently, ensuring scalable solutions for data-driven insights.
Design and implement big data machine learning solutions using technologies such as Spark, and Kafka for real-time data processing and analytics.
Explore and integrate new big data technologies and tools into the existing data ecosystem to improve data analytics and model development processes.
Your Mission:
As a Data Scientist, your mission will be to leverage your expertise in machine learning and generative AI to develop and implement models and Gen-AI tools that enhance our analytics, product development, and operational efficiency. You will play a crucial role in interpreting and analyzing complex data sets to provide actionable insights, drive strategic decision-making, and create cutting-edge innovative solutions that keep us at the forefront of the payment technology industry.
You will collaborate closely with data engineers, business analysts, and product teams to identify opportunities for leveraging company data in new ways. Through your work, you will enable us to harness the full potential of our data, ensuring that our strategies and products are informed by deep, data-driven insights.
Responsibilities:
Develop and implement advanced machine learning and generative AI models to solve complex business problems, enhance product offerings, and improve customer experiences.
Conduct data exploration and feature engineering to uncover hidden insights and identify opportunities for leveraging data across the business.
Collaborate with cross-functional teams to understand business needs and provide data-driven solutions and recommendations.
Stay abreast of the latest developments in machine learning and Gen-AI technologies, applying cutting-edge techniques and tools to drive innovation within.
Evaluate model performance and continuously iterate on and refine models to maintain their accuracy and relevance.
Communicate complex data concepts and the results of analyses clearly and effectively to stakeholders across the company.
Utilize big data technologies to process and analyze large datasets efficiently, ensuring scalable solutions for data-driven insights.
Design and implement big data machine learning solutions using technologies such as Spark, and Kafka for real-time data processing and analytics.
Explore and integrate new big data technologies and tools into the existing data ecosystem to improve data analytics and model development processes.
Requirements:
A Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or a related field with a strong focus on machine learning or artificial intelligence.
At least 3 years of hands-on experience in developing and implementing machine learning models, with a strong preference for candidates with experience in generative AI.
Proficiency in programming languages used in data science, such as Python, and familiarity with machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch).
Experience with data manipulation and analysis tools, including SQL, and experience working with large data sets.
Strong understanding of machine learning principles and algorithms, including supervised and unsupervised learning, deep learning, NLP, and generative models.
Demonstrated ability to apply machine learning techniques to real-world problems, from initial concept through data preparation, model development, and deployment.
Excellent analytical and problem-solving abilities, with a keen attention to detail and a data-driven approach to decision-making.
Exceptional communication skills, with the ability to convey complex data insights to both technical and non-technical stakeholders.
Familiarity with cloud computing services (AWS, Azure, or Google Cloud) and experience deploying models in a cloud environment is a plus.
A track record of continuous learning and adapting to new technologies and techniques in data science and AI.
A Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or a related field with a strong focus on machine learning or artificial intelligence.
At least 3 years of hands-on experience in developing and implementing machine learning models, with a strong preference for candidates with experience in generative AI.
Proficiency in programming languages used in data science, such as Python, and familiarity with machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch).
Experience with data manipulation and analysis tools, including SQL, and experience working with large data sets.
Strong understanding of machine learning principles and algorithms, including supervised and unsupervised learning, deep learning, NLP, and generative models.
Demonstrated ability to apply machine learning techniques to real-world problems, from initial concept through data preparation, model development, and deployment.
Excellent analytical and problem-solving abilities, with a keen attention to detail and a data-driven approach to decision-making.
Exceptional communication skills, with the ability to convey complex data insights to both technical and non-technical stakeholders.
Familiarity with cloud computing services (AWS, Azure, or Google Cloud) and experience deploying models in a cloud environment is a plus.
A track record of continuous learning and adapting to new technologies and techniques in data science and AI.
This position is open to all candidates.
