Sr. Applied Data Scientist

Applaudo; Rio de Janeiro; On-site; BRL 180,000 - 320,000; Full time

Job summary

Applaudo is seeking an experienced Data Scientist with strong applied machine learning expertise to build and evaluate models using large, messy datasets. The role involves embeddings, semantic similarity, LLMs, NLP, and both supervised and unsupervised learning.

You will design experiments, baselines, and error analyses and clearly communicate trade-offs to engineering and business stakeholders. You will be highly autonomous, honest about results, and contribute to scalable ML solutions from

Qualifications

• What you bring is a strong applied ML foundation and experience delivering real-world models.
• You should be proficient in Python and SQL and comfortable with embeddings and semantic similarity.
• Experience applying LLMs to practical problems and working with NLP tasks.
• Experience with supervised and unsupervised learning and model evaluation.

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 duplicates.
• Work with messy data including multilingual records and firmographic attributes.
• Define benchmarks, metrics, baselines, and error-analysis processes.
• Design and execute experiments to validate hypotheses and compare approaches.
• Analyze model behavior, edge cases, and scalability and cost considerations.
• Communicate findings to engineering and business stakeholders.

Skills

Applied ML fundamentals

Python

SQL

Embeddings

Semantic similarity

LLMs

NLP

Classification

Neural networks

TensorFlow

PyTorch

ML framework experience

Tools

TensorFlow

PyTorch

PyCaret

Spark

Snowflake

Databricks

BigQuery

Job description

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.

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.
• 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.

Additional Information

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 we work, how we support one another, and how we hold ourselves accountable.

Applaudo is a place for people who want to learn fast, take ownership, and work alongside strong teams they are proud to belong to. Joining us means being part of an organization that is evolving intentionally, investing in modern ways of working, and leading AI-native transformation at scale.