We are looking for Data Scientists who are great at problem-solving, prototyping, partnering with Engineers, and communicating recommendations effectively. Responsibilities You will work on client deliverables to solve business problems using the power of data and analytics. Your key responsibilities will be to - Understand the problem statement and use logical thinking to develop appropriate solutions. Gain knowledge and understanding of the client's business. Perform exploratory and statistical analyses to uncover key insights. Perform Feature Engineering and build Pipelines. Train and Deploy ML Models. Build dashboards to track model performance and key business metrics. Ensure the project meets deadlines without compromising on agreed deliverable quality. Requirements 1-3 years of hands-on experience in Analytics and Machine Learning to solve business problems. Understanding of different Statistical and ML algorithms is mandatory. Strong coding skills with proficiency in Python and SQL. Hands-on experience with deep learning algorithms in any one of these frameworks - PyTorch, TensorFlow, or fast.ai is a plus. Hands-on experience in using any of these Cloud Platforms - AWS/Azure/GCP is a plus. This job was posted by Akshay Singh from Yugen.ai.

We are looking for Backend Engineers with 1-3 years of production experience shipping and supporting backend code. You will be a part of our team, owning the real-time ingestion and analytics layer that powers customer-facing dashboards, trading tools, and research. Responsibilities Design, build, and scale streaming workflows that move and process multi-terabyte (and rapidly growing) datasets. Guarantee end-to-end reliability by owning performance, fault-tolerance, and cost efficiency from source to sink. Instrument every job with tracing, structured logs, and Prometheus metrics so every job tells you how it's doing. Publish Grafana dashboards and alerts for latency, throughput, and failure rates; act on them before users notice. Partner with DevOps to containerize workloads and automate deployments. Automate reconciliation checks, confirm completeness, and replay late or corrected records to maintain pristine datasets. Collaborate with stakeholders to verify data completeness, run automated reconciliation checks, and re-process late or corrected data. Requirements Proficient in Rust - comfortable with ownership, borrowing, async/await, cargo tooling, and profiling/optimisation. Stream-processing - have built or maintained high-throughput pipelines on NATS (ideal) or Kafka. Deep systems engineering skills - you think about concurrency models, memory footprints, network I/O, back-pressure, and graceful degradation; can instrument code with tracing, metrics, and logs. ClickHouse (or similar OLAP) - able to design MergeTree tables, reason about partitions / order-by keys, and optimise bulk inserts. Cloud- have deployed containerised workloads on AWS or GCP, wired up CI/CD, and tuned instance types or autoscaling groups. Nice-to-have - exposure to blockchain or high-volume financial data streams. This job was posted by Akshay Singh from Yugen.ai.