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Teccon GmbH & Co. KG

... durch Machine Learning und AI und arbeitest mit Tools wie SAP BDC und MS Power Plattform * Du ... SAP Business Data Cloud & MS Power Plattform * Du bringst mehrere Jahre Erfahrung im ...

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How much do data scientist machine learning jobs pay per year?

As of Sep 14, 2026, the average yearly pay for data scientist machine learning in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

What is a data scientist machine learning?

A Data Scientist specializing in Machine Learning (ML) uses statistical methods, algorithms, and computational power to analyze data and create predictive models. They work with large datasets to identify patterns, train machine learning models, and improve decision-making processes. Responsibilities often include data cleaning, feature engineering, model selection, and performance evaluation. They may collaborate with engineers and business teams to deploy models in real-world applications. Strong skills in programming (Python, R), ML frameworks (TensorFlow, Scikit-learn), and data visualization are essential.

What are the typical day-to-day responsibilities of a data scientist machine learning?

On a typical day, a Data Scientist specializing in Machine Learning might gather and preprocess data, design and implement machine learning models, and evaluate their performance to solve real-world problems. They often collaborate with data engineers, software developers, and business stakeholders to translate business objectives into technical solutions and integrate models into existing systems. Other responsibilities can include visualizing data insights, conducting experiments to tune algorithms, and staying current with new developments in the field. The work is highly collaborative and iterative, requiring clear communication with various teams to ensure project goals are met efficiently.

What are the key skills and qualifications needed to thrive in the data scientist machine learning position, and why are they important?

To excel as a Data Scientist Machine Learning, you need a strong proficiency in statistics, programming (typically Python or R), and a solid understanding of machine learning algorithms, usually backed by a degree in computer science, mathematics, or a related field. Familiarity with tools such as TensorFlow, scikit-learn, SQL databases, and cloud platforms, as well as certifications in data science or machine learning, is commonly expected. Analytical thinking, problem-solving skills, and effective communication are vital soft skills in this profession. These qualifications combine to drive impactful insights and enable the successful development and deployment of machine learning models in business environments.

What is the salary of data scientist in machine learning?

The salary of a data scientist specializing in machine learning typically ranges from $90,000 to $150,000 annually, depending on experience, location, and industry. Senior roles or those with advanced skills in programming, statistical analysis, and tools like Python or TensorFlow may earn higher compensation.
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Data Scientist Machine Learning jobs near you
Infographic showing various Data Scientist Machine Learning job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 84% Full Time, 12% Part Time, and 2% Contract. Highlights an 85% Physical, 3% Hybrid, and 12% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.

Senior Data Scientist / Machine Learning Engineer, NLP - 1635

Calabasas, CA • On-site

aKube
See more jobs at aKubeexternal

$85/hr

Other

Re-posted 29 days ago

Job description

Senior Data Scientist / Machine Learning Engineer, NLP - 1635

Calabasas, United States | Posted on 07/15/2026

Onsite/ Hybrid/ Remote: Hybrid Calabasas (Monday-Wednesday in office) , Las Vegas (5 days onsite)

Duration: 6 months
Rate Range: Upto $85/hr on W2
Work Authorization: GC, USC, All valid EADs except H1B, OPT, CPT

Must Have:

  • 4–6+ years of data science or machine learning experience
  • NLP classification for customer messages or call transcripts
  • Intent, topic, sentiment, and multi-label classification
  • Confidence scoring and model evaluation
  • Text cleaning, deduplication, speaker handling, and PII-safe processing
  • Trend and anomaly detection
  • Python, PySpark, SQL, and pandas
  • Labeled dataset design and annotation workflows
  • Precision, recall, confusion matrix, and drift monitoring

Responsibilities:

  • Build and deploy NLP classification models for customer communications.
  • Develop intent, topic, sentiment, and multi-label taxonomies.
  • Clean and prepare transcript and message data for modeling.
  • Handle short-text cases, duplicate records, system messages, and speaker identification.
  • Build trend and anomaly detection methods using baselines, seasonality, and channel mix.
  • Design maintainable Python and PySpark data pipelines.
  • Define sampling strategies and annotation guidelines for labeled datasets.
  • Support reviewer adjudication and dataset quality validation.
  • Track model precision, recall, confusion patterns, confidence scores, and drift.
  • Implement secure processing for customer communications containing sensitive data.

Qualifications:

  • 4–6+ years of relevant machine learning, NLP, or data science experience.
  • Proven experience deploying NLP models into production.
  • Strong experience with classification systems and text analytics.
  • Advanced Python development and testing skills.
  • Hands-on experience with PySpark, SQL, pandas, and scalable data pipelines.
  • Experience creating and validating labeled datasets.
  • Strong understanding of model evaluation, monitoring, and false-alert reduction.
  • Experience working with governed or PII-bearing data.

Nice to Have:

  • Databricks
  • Unity Catalog
  • Databricks Workflows
  • MLflow
  • Model and data versioning
  • Retrieval and embedding models
  • LLM-assisted classification with evaluation and guardrails
  • Contact-center or customer-support analytics
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