Research Scientist Junior/Junior+
我们正在寻找一名年轻的研究科学家来为金融数据做工作。其中一些已经在局，交易和图表数据上生产；你的工作是将这一线扩展到新的数据域和新下游任务信用风险，收购，防欺诈，LTV 预测。我们与 Python, PyTorch,MLFlow, AWS,Snowflake 以及现代 AI 编码工具如 Cloade Code,OpenAI Codex 和 Cursor 合作。这是一个很好的机会，对于那些开始职业生涯的人来说，希望在大型技术挑战性项目上工作。你面临的挑战：· 训练自主监督模型在单独的序列上以击败 SOTA 并实现业务影响，在 Plata 各地下游任务中，如：信用风险，反欺诈交易，收购和 LTV 预测 ·保持 SOTA 研究的顶部，将最新的 NLP 和 DL 技术应用到金融科技模型 ·与大型多模式数据集合作：表格，行为，交易，设备和网络，文字，时间系列，图形 ·优化利用计算资源进行培训和推断 ·从想法到数据收集，实验到培训运行，对推断优化进行评估；执行严格的评估。· 写文章和在行业会议上发表讲话：• 教育：数学，工程，计算机科学，人工智能或其他强大的量化领域 · 机器学习和深度学习的基础坚固，实践经验建立和使用 PyTorch 和 LLM 模型进行试验 · 对数据集设计和数据混合进行深度学习培训 · 如何最大限度地利用计算资源并有效执行研究想法的实验 · 健全沟通技能 · 高自主性和拥有能力 你的奖励技巧：· 机器学习或深度学习的研究发表 · 人工智能，机器学习和深度学习领域的个人项目或创业经验 · 研究人员实习经验，ML 或 DL 团队·与国际团队有效沟通的英语水平 B1以上 我们的工作方式：·创新精神：致力于创造性和开创性的解决方案 ·诚实的反：重视公开透明通信 ·支持团队：强大协作社区 ·庆祝成就：承认我们的目标
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We are looking for a Junior Research Scientist to work on foundation models for financial data. Several of them already run in production on bureau, transactional and graph data; your work is to extend that line to new data domains and new downstream tasks — credit risk, acquisition, anti-fraud, LTV forecasting.
We work with Python, PyTorch, MLFlow, AWS, Snowflake and modern AI coding tools such as Claude Code, OpenAI Codex and Cursor.
This is a great opportunity for someone at the beginning of their career and who wants to work on large-scale, technically challenging projects.
Challenges that await you:
- Train self-supervised models on discrete sequences to beat the SOTA and achieve business impact in downstream tasks across Plata, such as: credit risk, transaction anti-fraud, acquisition and LTV forecasting
- Stay on top of SOTA research, applying the latest NLP and DL techniques to fintech models
- Work with large multimodal datasets: tabular, behavioral, transactional, device and network, text, time series, graphs
- Optimize the utilization of compute resources for both training and inference
- Own and develop solutions end-to-end, from idea to data collection to experiments to training runs to inference optimization to evaluating impact
- Perform rigorous evaluations
- Write articles and speak at industry conferences
What makes you a great fit:
- Education: mathematics, engineering, computer science, artificial intelligence or another strong quantitative field
- Strong foundation in machine learning and deep learning, with hands-on experience building and experimenting with models using PyTorch and LLMs
- Understanding of dataset design and data mixing for deep learning training
- Demonstrated understanding of how to maximize the utilization of compute resources and efficiently perform experiments to validate research ideas
- Solid communication skills
- High autonomy and ownership
Your bonus skills:
- Published research in machine learning or deep learning
- Personal projects or startup experience in AI, machine learning or deep learning
- Internship experience in research, ML or DL teams
- B1 or higher English level for effective communication with an international team
Our ways of working:
- Innovative Spirit: A commitment to creativity and groundbreaking solutions
- Honest Feedback: valuing open, transparent communication
- Supportive Team: a strong, collaborative community
- Celebrating Achievements: recognizing our wins together
- High-Tech Environment: a team full of smart and revolutionary people who dare to challenge the status quo of incumbent finances
Our benefits:
- Relocation support to one of our hubs — Serbia, Georgia or Mexico — with assistance for the employee and their family
- Flexible work from one of our offices or remotely within time zones from GMT-2 to GMT+5
- Healthcare Coverage
- Education Budget: Language lessons, professional training and certifications
- Wellness Budget: Mental health and fitness activity reimbursements
- Vacation policy: 20 days of annual leave and paid sick leave
Originally posted on Himalayas