Applaudo Studios
Data Scientist
Remote role where the employee must remain based in a particular country.
Peru only
Employer listed it 3 weeks ago · Added today
Been open since 3 weeks ago, still being checked, but it has been live a while.
Salary
Not stated
Location
Peru only
Timezone
Not stated
Contract
Full-time
Experience
Mid
Category
Data
This employer didn't state pay. Jobs like this usually pay around $115k–$215k a year, a typical range taken from 180 mid-level data roles on Nomaders that do state pay. It's a guide, not an offer.
Remote flexibility
Work from home
This is a remote role, but the employee must be based in Peru. It is work from home rather than work from anywhere.
What the employer says
- Source listing states candidate location: "Lima, Callao Region, Peru, Lima, Callao Region, pe, Remote"
What Nomaders makes of it
- Residency required in Peru
- Payroll and tax are likely handled in that country only
The quotes above are the employer's own words; the reading is ours. Always check the original listing and employment terms before working from another country.
About the role
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.
Requirements
- ·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.
Benefits
No benefits package published with this listing. Ask about it at first interview.
How to apply
- 1Check the flexibility label above, work from home, matches where you plan to live and work.
- 2Tailor your CV to the role at Applaudo Studios, mentioning your remote working experience.
- 3Apply directly on the employer's careers page using the button below. Nomaders never handles your application.
Found 15h ago. Last checked today. Always confirm the details on the original posting, salary and location can change after publication.
Listing sourced from Company boards.
Similar roles
Other open data roles with comparable remote rules.
Free to apply, no account needed.
Typically $115k to $215k per year · You'll be taken to the employer's careers page.