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Senior Applied Data Scientist

Reads like a ML Engineer role: putting models into production.

Lima, Callao Region, Peru

Live, first seen by us 20 days ago
Pay
Pay not listed
Work setting
Remote — work setting source
Work setup in the employer’s posting: Remote
From the employer’s posting
Employment
Full-time — employment source
Employment type in the employer’s posting: Full-time
From the employer’s posting

What you’ll work on

Full posting
  • Build and evaluate ML approaches for company and entity matching

  • Develop embedding and LLM-based matching approaches

  • Develop scoring and ranking to separate true matches from duplicates and lookalikes

  • Define benchmark datasets, metrics, baselines, and error-analysis processes

  • Compare LLM-assisted approaches against lower-cost alternatives

From the employer’s posting
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.
Define benchmark datasets, metrics, baselines, and error-analysis processes.
Compare LLM-assisted approaches against lower-cost alternatives.

What you’ll bring

All qualifications

Core experience

  • 5+ years of professional Data Science or ML experience
  • Hands-on experience with embeddings and semantic similarity
  • Excellent Python and SQL skills
  • Practical experience applying LLMs to real-world problems
  • Hands-on experience with TensorFlow, PyTorch, PyCaret, or equivalent
Qualification wording
5+ years of professional Data Science / Machine Learning experience.
Hands-on experience with embeddings and semantic similarity.
Excellent Python and SQL skills.
Practical experience applying LLMs to real-world problems.
Hands-on experience with TensorFlow, PyTorch, PyCaret, or equivalent ML frameworks.

About Applaudo Studios

At Applaudo, talented people design, build, and scale meaningful, AI-powered solutions that create real business impact.

In the employer’s words · Read in context

Apply at Applaudo Studios

Posting record

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Status in our records
Live
First seen by us
Oct 15, 202620 days ago
Recorded sightings
26
Last seen by us
Nov 4, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Time since we first saw it

Day 20

Similar jobs usually come down between day 6 and day 69.

From 4,296 similar postings that came down. Coming down is not always a hire.

Applaudo Studios is hiring

5 open jobs

Open jobs by the week we first saw them, last 12 weeks.

  • Hiring is picking up
  • Takes jobs down at a usual pace
  • 20% of open jobs were reposted
Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

  • Python
  • SQL
  • BigQuery
  • Databricks
  • Snowflake
  • Spark
  • PyTorch
  • TensorFlow
Where the posting names them
- Strong applied Machine Learning fundamentals. - Excellent Python and SQL skills. - Hands-on experience with embeddings and semantic similarity.
- LLM/embedding solutions designed for cost and scale constraints. - Spark, Snowflake, Databricks, or BigQuery. - Experience with company, domain, website, or firmographic data.
- 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.

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About the role

We file this job under ML Engineer, by the work the posting describes.

ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.

Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.

A data scientist often works out which model to build; an ML engineer makes it run reliably in production.

US government figures for computer and information research scientists

Median yearly pay, May 2025
$140,300
Expected change, 2025 to 2035
21.8% more jobsagainst 3.5% for all jobs

The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.

Job description

View original posting ↗

Job Description

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.

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.

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Lima, Callao Region, Peru

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