Sr. Applied Data Scientist
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Before you apply
LocationLima, Callao Region, pe
Work moderemote · Peru
Typefull-time
SenioritySenior
Experience5+ yrs
DepartmentTechnology
Company size501–1,000 people
PostedOct 14, 2026 · 3w ago
Verified live2d ago
At a glanceSummarised by Seekless from the posting.
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
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
Skills
Python
SQL
TensorFlow
PyTorch
PyCaret
Spark
Snowflake
Databricks
BigQuery
Job Description
About You
About the role
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
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Build and evaluate ML approaches for company/entity matching.
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Develop embedding and LLM-based matching approaches.
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Develop scoring and ranking methodologies to identify true matches and distinguish them from duplicates, lookalikes, and unrelated entities.
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Work with messy data, including names, aliases, domains, websites, firmographic attributes, multilingual records, and data hierarchies.
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Define benchmark datasets, metrics, baselines, and error-analysis processes.
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Design and execute experiments to validate hypotheses.
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Compare LLM-assisted approaches against lower-cost alternatives.
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Analyze model behavior, edge cases, and trade-offs.
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Consider inference economics and scalability from the beginning.
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Communicate experimental findings and recommendations to engineering and business stakeholders.
•
Independently establish experimental pipelines and research approaches.
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Clearly document both successful and unsuccessful experiments.
What Sets You Apart
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Strong analytical and experimental mindset.
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Intellectual honesty and willingness to communicate negative results.
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Strong autonomy and self-direction.
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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.
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.