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RiDiK (a Subsidiary of CLPS. Nasdaq: CLPS)

Data Scientist

RiDiK (a Subsidiary of CLPS. Nasdaq: CLPS)

Singapore · Kontrak

Jadilah yang pertama mendaftar

Pengalaman
3+ tahun
Gaji
Lowongan
1
Diposting
4 jam yang lalu
Mode kerja
Di kantor
Pendidikan
Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or related fields
Melanjutkan
Wajib mendaftar

Tempat Anda akan bekerja

Deskripsi pekerjaan

Role Overview

We are seeking an experienced Data Scientist with over three years of professional expertise in data science with recognized organizations. The ideal candidate will hold a Bachelor's or Master's degree in relevant quantitative fields such as Computer Science, Data Science, Statistics, Mathematics, or Engineering.

Key Responsibilities and Expertise

  • Demonstrate advanced problem-solving capabilities tailored to data science challenges, utilizing structured approaches like MECE and root cause analysis.
  • Possess hands-on experience and familiarity with MLOps frameworks, especially overseeing complete model lifecycle management and deployment within cloud environments such as AWS.
  • Deep knowledge and practical application of deep learning methodologies including architectures like transformers and reinforcement learning.
  • Expertise in AI engineering concepts, notably neural embeddings, large language models (LLMs), and the construction of generative AI systems using frameworks like LangChain and LangGraph, including integration with vector databases and Extended Data Discovery (EDD).
  • Skilled in optimization techniques, statistical modeling, and multi-criteria decision analysis that enhance data-driven solutions.
  • Competent in data engineering practices supporting data science, such as ELT processes, structured data transformations, and proficiency with various database technologies.
  • Exhibit strong teamwork spirit as well as excellent communication and data visualization skills to effectively collaborate and convey insights.
  • Proficient in programming languages and tools fundamental to data science workflows, including Python (with associated frameworks), R, SQL, and Shell scripting.

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