Senior Data Scientist

We are looking for a Senior Data Scientist who can lead the end-to-end delivery of data science, analytics, and data engineering projects for our clients. You will guide a team of junior data scientists and analysts, translating client problems into working solutions built on our suite of data platforms - and rolling up your sleeves to write Python when the tooling alone isn’t enough.

This is a hands-on leadership role. You will spend part of your time mentoring and reviewing your team’s work, part of it engaging clients and managing delivery, and part of it in the weeds: designing pipelines, building models, and stitching platform components together with custom code.

What You’ll Do

Project & Technical Delivery

• Lead the execution of client-facing data science, analytics, and data engineering projects from scoping through deployment and handover.
• Design analytical approaches and solution architectures using our low-code data science studio, visual ETL platform, and enterprise lakehouse (with change-data-capture capabilities).
• Write Python to extend, integrate, or bridge these platforms - custom transformations, model logic, APIs, automation, and anything the visual tools can’t handle out of the box.
• Build and operate the harder engineering layers of a solution when projects demand it: orchestrated pipelines, worker-based and event-driven architectures, stream processing, and scaling for data volume and concurrency.
• Own solution quality: data validation, model evaluation, reproducibility, performance, and operational reliability (monitoring, alerting, failure recovery).

Team Leadership

• Lead and mentor a team of junior data scientists and analysts; review their work, unblock them technically, and grow their skills.
• Break projects down into workable tasks, assign them across the team, and keep delivery on track.
• Set and uphold standards for code quality, documentation, and analytical rigor.

Stakeholder & Client Management

• Serve as the primary technical point of contact for client stakeholders during project delivery.
• Translate ambiguous business problems into well-defined analytical work, and communicate findings and trade-offs clearly to non-technical audiences.
• Manage scope, timelines, and expectations; escalate risks early and propose options.

Internal Collaboration

• Work alongside our internal product engineering team (Python/FastAPI backend, React frontend) to feed field learnings back into the product and, occasionally, contribute to backend integrations.

• 5+ years of experience in data science and analytics, with at least 1-2 years leading or mentoring junior team members.
• Strong, practical Python skills: pandas/NumPy, scikit-learn, data pipeline scripting, working with APIs, and writing maintainable code others can build on. Strong familiarity with Python backend frameworks (either or a combination of FastAPI, Django, Flask)
• Solid grounding in statistics, machine learning, and analytical problem-solving - you can choose the right method for the problem, not just the fashionable one.
• Proficiency in SQL and comfort working with large datasets in modern data platforms (lakehouse architectures, columnar stores, CDC-based ingestion).
• Hands-on experience building and operating data orchestration pipelines (e.g., Airflow, Dagster, Prefect, or equivalent) - scheduling, dependency management, retries, backfills, and monitoring.
• Working knowledge of distributed compute frameworks (e.g., Spark, Dask) and when to reach for them versus simpler approaches.
• Demonstrated project delivery experience: scoping, planning, managing stakeholders, and shipping on time.
• Excellent communication skills, both with technical teams and business stakeholders.

Nice-to-haves

• Exposure to AI engineering: LLM applications, RAG pipelines, prompt engineering, or model serving. This is a very strong nice-to-have for us, as we are also going big into developing AI applications and data platforms.
• Experience designing beyond single-machine batch jobs: distributed workers and task queues (e.g., Celery, RQ), stream processing (e.g., Kafka, Flink, Spark Structured Streaming), and scaling pipelines for throughput and reliability.
• Experience with low-code/visual data science or ETL tools (e.g., Dataiku, Alteryx, KNIME, or similar) and a clear sense of when to use them versus custom code.
• React/frontend literacy.
• Experience with containerized deployments (Docker) and CI/CD practices.
• Consulting or client-services background.

What We Expect

• Within 3 months: take ownership of at least one active client project, know our platform stack well enough to design solutions on it, and have established a working rhythm with the team.
• Within 6 months: Independently leading multiple concurrent projects, juniors under you are visibly leveling up, and clients ask for you by name.
• Within 12 months: you’ve shaped how we deliver data science projects - reusable patterns, better standards, faster ramp-up for new team members.

DataMicron is a multi-award Big Data and AI company, with patented technologies in USA and China. We have extensive experience in developing products and full-scale solutions in the field of business intelligence, machine learning & AI, data engineering and front-end development. We have covered and implemented end-to-end Big Data Analytics solutions in eight countries across major public and private sector organizations, and are currently going through a large expansion in our business portfolio.