Job description

AI/ML Data Scientist - Exp. with DeepSeek/OpenAI/Claude

Employment Type: Full-Time Location: Remote in Singapore

Minimum Experience: Entry Level

Pixalate is an online trust and safety platform that protects businesses, consumers and children from deceptive, fraudulent and non-compliant mobile, CTV apps and websites.

Our software and data have been used to unearth multiple high profile criminal and illegal surveillance cases including:

Gizmodo: An iCloud Feature Is Enabling a $65 Million Scam, New Research Says
Adweek: A 7-Figure Ad Fraud Scheme Running on Roku Underlines Murkiness of CTV
Washington Post: Your kids’ apps are spying on them
Pro Publica: Porn, Piracy, Fraud: What Lurks Inside Google’s Black Box Ad Empire
ABC7 News: The State of Children's Privacy Online
NBC News: How many apps are tracking your children

Our team of lawyers, data scientists, engineers, economists and researchers span globally with presence in California, New York, Washington DC , London and Singapore.

ABOUT THE ROLE

In this role, you will collaborate closely with data scientists, big data engineers, product managers, and marketers, as well as legal, engineering, marketing, sales, and customer success teams. You will analyze large datasets, develop AI/ML algorithms, and derive insights to address complex business problems in fraud detection/prevention and advertising data analytics, while ensuring compliance with global privacy and data protection laws. As the ad tech industry undergoes a major shift towards greater transparency and protection against fraud, Pixalate is uniquely positioned as the first MRC-accredited, omni-channel fraud protection provider focused on reducing fraud, not just measuring it.

QUALIFICATION

BS in Computer Science, Electrical Engineering, Math, Physics, or other quantitative fields
Experience in data science, solving complex problems with a focus on AI/ML techniques and privacy expertise
Experience in implementing AI/ML algorithms, including deep learning and reinforcement learning models, while ensuring compliance with privacy and data protection laws
Ability to work independently and solve complex problems with minimal supervision
Strong critical thinking and ability to generate intuitive conclusions from complex datasets
Excellent verbal and written communication skills, with a high attention to detail
Proficiency in SQL and Python
Solid understanding of machine learning algorithms, basic probability, and statistics
Familiarity with AI/ML tools and frameworks (e.g., OpenAI, DeepSeek, ChatGPT, TensorFlow, PyTorch, Keras)

BONUS QUALIFICATIONS

Preferred Familiarity with online advertising, advertising fraud, and global privacy and data protection laws, with a focus on European privacy and data protection laws and regulations
Preferred MS or PhD degree in Computer Science, Electrical Engineering, Math, Statistics, or other quantitative fields with a focus on AI/ML and privacy expertise
Preferred Familiarity with Amazon AWS, UNIX CLI, and programming in Scala, Java, and/or C/C++
Preferred Proven track record of integrating privacy-by-design principles into data science projects and product

RESPONSIBILITIES

Leverage advanced machine learning techniques, including deep learning and reinforcement learning, to improve fraud detection and prevention models
Leverage LLMs, VLMs, BERT, and CLIP models to create new and innovative products, explore new techniques and improve overall product suite
Research and implement state-of-the-art AI/ML algorithms to stay ahead of emerging fraud patterns and trends, maintaining compliance with privacy laws and regulations
Provide technical support for existing products by resolving complex client issues through coordinated tickets and data analysis
Identify new types of fraud and develop early detection/prevention algorithms using AI/ML techniques
Create visualizations, document methodologies, and effectively communicate research insights to product management and marketing teams, while maintaining privacy compliance
Develop and test statistical hypotheses to support data-driven decision-making
Fine-tune existing ML and LLM models to improve accuracy, speed and efficiency