Data Scientist

At Aplazo, our Data Science team is a cornerstone of our technological innovation and strategic decision-making. We pride ourselves on our technical maturity and high proficiency in addressing complex and critical business challenges. Our team is dedicated to solving high-stakes problems across various domains, including risk and payments, merchant, B2C, personalization, growth and marketing, recommendation systems, customer support, collections, underwriting, credit life cycle management, and fraud detection.

Aplazo data science lab has been instrumental in the development of state-of-the-art credit risk and fraud detection models. The risk and payment machine learning models suite has leveraged advanced data science and AI algorithms to achieve best-in-class prediction capabilities. Aplazo has adopted best practices in MLOps, creating a seamless workflow for model development, deployment, and monitoring. This includes automated CI/CD pipelines, version control for models and data, and robust monitoring and logging systems. We have built a sophisticated ML engineering infrastructure that supports scalable model training, testing, and deployment. This infrastructure ensures the high availability, reliability, and performance of our machine-learning solutions.

Responsibilities

• Develop highly scalable credit and fraud risk models and tools leveraging machine learning, deep learning, and rules-based models.
• Work closely with product and operation teams to implement new risk reduction practices using ML and DL.
• Build state-of-the-art risk models using alternative data such as Android device data, bank statement data, etc.
• On the data science model end-to-end, from data collection to model building to monitoring the model in production.
• Build machine learning and deep learning models in the customer lifecycle, which include personalization, recommendation, rewards, referrals, transaction categorization, and customer science-related models.
• Understand the End-to-End ML pipeline (data gathering to production) basic understanding of production-level coding practices
• Data acquisition and stakeholder management: evaluate and identify superior data sources, perform ROI estimations for data vendor partnerships, and collaborate with commercial and business teams to align data strategies and meet their needs.
• Conduct data analyses; your analyses will decide which policies we adopt, where we expand our business, and with whom we partner.
  
  

Requirements

• Languages: English (Level: advanced).
• 1 to 4 years of relevant work experience.
• Experience in statistical modeling, machine learning, data mining, unstructured data analytics, and natural language processing.
• Sound understanding of Bayesian Modeling, Classification Models, Cluster Analysis, neural networks, Nonparametric Methods, multivariate statistics, NLP, etc.
• Experience with common libraries and frameworks in data science.
• Familiarity with pre-trained language models (e. g., BERT, GPT-3 GPT-4 T5).
• Production level deployment (ML engineering, OOPs).
• Collaborate with Platforms and Engineering to make sure the ML models built are deployed and integrated into the systems.
• Ability to work in a high-ownership environment; the right candidate should have the competency to work independently as well as collaboratively with the team.
• Experience in risk management within consumer lending, buy-now-pay-later, payment, or credit card sectors is a plus but not mandatory.
  
  

Technical Skills

• Proficient in Python programming, with a focus on coding for machine learning and deep learning applications.
• Strong in data analysis and data wrangling.
• Experience with database queries and data analysis processes (SQL, Python).
• Follow industry best practices and stay up to date with the latest machine learning/GenAI algorithms and techniques to drive innovation.
• Knowledge of deep learning concepts like CNN, RNN, tokenization, transformers, and various NLP techniques.
  
  

Soft Skills

• Highly curious and passionate about problem-solving, with a strong drive to uncover insights and optimize solutions.
• Exceptionally detail-orientated, capable of working both independently and collaboratively in a fast-paced environment.
• Excellent communication skills, with the ability to translate complex concepts into clear, actionable insights for both technical and non-technical stakeholders.
• Energetic and proactive, with a strong learning aptitude and the audacity to take on challenges beyond their comfort zone.
• Not afraid to go above and beyond, demonstrating initiative and ownership in tackling tough problems.
  
  

Academic Background (studies Or Certifications)

• Bachelor's or Master's degree in Computer Science, Information management, Statistics, or a related field
• A Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or a related field AND 2+ years of data science experience.
  
  

Nice To Have

• Publications or presentations in recognized Machine Learning and Data Science journals/conferences.
• Experience with cloud services (like AWS or Google Cloud) and understanding of distributed systems.
• Exposure to GenAI models.
  
  

This job was posted by Ankur Pal from Aplazo.