全职 Data Scientist Manager 工作, 薪水 up to MYR 16,000, Datamicron Systems Federal Territory 公司招聘中 - Ricebowl

Responsibilities 

Project & Technical Delivery 

• Lead client-facing data science, analytics, and data engineering projects from scoping through deployment and handover. 
• Design solution architectures using our low-code data science studio, visual ETL, and lakehouse with CDC, balancing maintainability and scale. 
• Write maintainable Python to extend and integrate platforms, build APIs, custom transformations, model logic, and automation where visual tools are insufficient. 
• Build and operate engineering layers when required, including orchestrated pipelines, worker-based and event-driven systems, stream processing, and scaling for volume and concurrency. 
• Own solution quality through data validation, model evaluation, reproducibility, monitoring, alerting, and recovery planning. 

Team Leadership & Delivery Management 

• Mentor junior data scientists and analysts, review their work, and remove technical blockers so the team can deliver reliably. 
• Break projects into clear tasks, assign work, track progress, and keep deliveries on schedule. 
• Set and enforce standards for code quality, documentation, and analytical rigor across the team. 
• Within 3 months take ownership of at least one active client project, know our platform stack, and establish a working rhythm with the team. 
• Within 6 months independently lead multiple projects, show visible development of juniors, and earn client recognition. 
• Within 12 months help shape how we deliver data science projects through reusable patterns and improved standards. 

Stakeholder & Product Collaboration 

• Serve as the primary technical contact for clients and translate ambiguous business problems into well scoped analytical plans. 
• Communicate findings, trade-offs, and risks clearly to non-technical stakeholders and propose practical options. 
• Collaborate with our product engineering team (Python/FastAPI backend, React frontend) to feed field learnings into the product and support backend integrations when needed. 

Requirements 

Qualifications & Experience 

• 5+ years of experience in data science and analytics, with at least 1 to 2 years leading or mentoring junior team members. 
• Proven project delivery record: scoping, planning, stakeholder management, and shipping solutions on time. 

Hard Skills & Knowledge 

• Strong, practical Python skills including pandas, NumPy, scikit-learn, API integration, and writing maintainable code others can build on. 
• Familiarity with Python backend frameworks such as FastAPI, Django, or Flask. 
• Solid grounding in statistics and machine learning, able to choose the right method for the problem. 
• Proficiency in SQL and comfort with large datasets on modern data platforms, including lakehouse architectures, columnar stores, and CDC-based ingestion. 
• Hands-on experience building and operating orchestration pipelines using Airflow, Dagster, Prefect, or equivalent, including scheduling, retries, backfills, and monitoring. 
• Working knowledge of distributed compute frameworks like Spark or Dask and sound judgement on when to use them versus simpler approaches. 
• Preferred experience: AI engineering exposure such as LLM applications, RAG pipelines, prompt engineering, or model serving. 
• Preferred experience: distributed workers and task queues (e.g., Celery, RQ), stream processing (e.g., Kafka, Flink, Spark Structured Streaming), low-code/visual ETL tools, React/frontend literacy, and containerised deployments with CI/CD. 
• Consulting or client services experience is a plus. 

Soft Skills & Qualities 

• Excellent communication skills with both technical teams and business stakeholders. 
• Strong analytical problem-solving and pragmatic decision-making under ambiguity. 
• Collaborative mentor and team player who raises standards through clear feedback and support.

 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.

Why Join Us

• Work across a genuinely varied portfolio of client problems rather than one narrow domain.
• Have the opportunity to involve in AI Engineering and software development
• A small, senior-heavy engineering culture where your technical opinions carry weight.

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.