Data Scientist

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About You You are an experienced Data Scientist with strong applied Machine Learning expertise and a track record of building and evaluating models using real-world, messy, large-scale data. You are comfortable working with embeddings, semantic similarity, LLMs, NLP, classification, and both supervised and unsupervised learning. You approach ambiguous problems through structured experimentation, clearly defined hypotheses, baselines, metrics, and error analysis. You are highly autonomous, intellectually honest about experimental results, and able to clearly communicate technical recommendations and trade-offs to engineering and business stakeholders. You Bring to Applaudo the Following Competencies 5+ years of professional Data Science / Machine Learning experience. Strong applied Machine Learning fundamentals. Excellent Python and SQL skills. Hands-on experience with embeddings and semantic similarity. Practical experience applying LLMs to real-world problems. Experience with supervised and unsupervised learning. Strong experience with classification and NLP. Working knowledge of neural networks and transformer architectures. Hands-on experience with TensorFlow, PyTorch, PyCaret, or equivalent ML frameworks. Experience retraining or maintaining classification models in production. Strong experimental design and model evaluation skills. Experience defining baselines, metrics, test sets, and error-analysis processes. Ability to evaluate model quality and demonstrate measurable improvements. Strong understanding of scalability and ML inference costs. Strong English communication skills. Nice-to-Have Entity resolution, record linkage, or deduplication experience. Ranking and similarity scoring. Retrieval, clustering, or candidate-generation techniques. LLM/embedding solutions designed for cost and scale constraints. Spark, Snowflake, Databricks, or BigQuery. Experience with company, domain, website, or firmographic data. Experience working with multilingual datasets. You Will Be Accountable for the Following Responsibilities Build and evaluate ML approaches for company/entity matching. Develop embedding and LLM-based matching approaches. Develop scoring and ranking methodologies to identify true matches and distinguish them from duplicates, lookalikes, and unrelated entities. Work with messy data, including names, aliases, domains, websites, firmographic attributes, multilingual records, and data hierarchies. Define benchmark datasets, metrics, baselines, and error-analysis processes. Design and execute experiments to validate hypotheses. Compare LLM-assisted approaches against lower-cost alternatives. Analyze model behavior, edge cases, and trade-offs. Consider inference economics and scalability from the beginning. Communicate experimental findings and recommendations to engineering and business stakeholders. Independently establish experimental pipelines and research approaches. Clearly document both successful and unsuccessful experiments. What Sets You Apart Strong analytical and experimental mindset. Intellectual honesty and willingness to communicate negative results. Strong autonomy and self-direction. Excellent written and verbal communication. Ability to defend technical recommendations with stakeholders. Strong problem-solving skills. Comfort working with ambiguity and large-scale datasets. Ability to balance model quality, cost, and scalability. About Us We Are Engineered Different. At Applaudo, talented people design, build, and scale meaningful, AI-powered solutions that create real business impact. As an AI-native organization, we collaborate across design, development, cloud, data, and artificial intelligence to turn ideas into scalable products that transform how companies operate, make decisions, and grow. We are building a high-performance culture grounded in five values: Empowering Excellence, Collaborative Teamwork, Unsolicited Respect, Consistent Transparency, and Efficient Communication . These define how w

关于你 你是一位经验丰富的数据科学家，具备扎实的应用机器学习专业知识，并有使用真实世界中杂乱、大规模数据构建和评估模型的良好记录。你能够熟练处理embeddings、语义相似度、LLMs、NLP、分类以及监督学习和无监督学习。你通过结构化实验、清晰定义的假设、基线、指标和错误分析来应对模糊问题。你高度自主，对实验结果保持理智上的诚实，并能够清晰地向工程和业务利益相关者传达技术建议和权衡。 你为Applaudo带来以下能力 5年以上专业数据科学/机器学习经验。 扎实的应用机器学习基础。 出色的Python和SQL技能。 具有embeddings和语义相似度的实操经验。 具有将LLMs应用于现实问题的实践经验。 具有监督学习和无监督学习经验。 在分类和NLP方面有丰富经验。 具备神经网络和transformer架构的工作知识。 具有TensorFlow、PyTorch、PyCaret或同等ML框架的实操经验。 具有在生产环境中重新训练或维护分类模型的经验。 出色的实验设计和模型评估技能。 具有定义基线、指标、测试集和错误分析流程的经验。 能够评估模型质量并展示可衡量的改进。 对可扩展性和ML推理成本有深入理解。 出色的英语沟通能力。 加分项 实体解析、记录链接或去重经验。 排序和相似度评分。 检索、聚类或候选生成技术。 针对成本和规模约束设计的LLM/embedding解决方案。 Spark、Snowflake、Databricks或BigQuery。 具有公司、域名、网站或企业画像数据经验。 具有多语言数据集工作经验。 你将负责以下职责 构建并评估用于公司/实体匹配的ML方法。 开发基于embedding和LLM的匹配方法。 开发评分和排序方法，以识别真实匹配并将其与重复项、相似项和无关实体区分开来。 处理杂乱数据，包括名称、别名、域名、网站、企业画像属性、多语言记录和数据层级。 定义基准数据集、指标、基线和错误分析流程。 设计并执行实验以验证假设。 将LLM辅助方法与低成本替代方案进行比较。 分析模型行为、边缘情况和权衡。 从一开始就考虑推理经济性和可扩展性。 向工程和业务利益相关者传达实验结果和建议。 独立建立实验流水线和研究方法。 清晰记录成功和失败的实验。 你的独特之处 强大的分析和实验思维。 理智上的诚实，并愿意传达负面结果。 高度自主和自我驱动。 出色的书面和口头沟通能力。 能够向利益相关者捍卫技术建议。 强大的问题解决能力。 能够从容应对模糊性和大规模数据集。 能够平衡模型质量、成本和可扩展性。 关于我们 我们以不同的方式构建。 在Applaudo，优秀的人才设计、构建和扩展有意义的、AI驱动的解决方案，创造真实的业务影响。作为一家AI原生组织，我们跨设计、开发、云、数据和人工智能进行协作，将想法转化为可扩展的产品，改变公司运营、决策和增长的方式。我们正在打造一种以五项价值观为基础的高绩效文化：赋能卓越、协作团队合作、主动尊重、始终透明和高效沟通。这些价值观定义了我们如何

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