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Open nowPosted 28 hours ago

Senior Data Scientist

Devsinc9 open roles

Where
Lahore, Punjab, Pakistan
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Your applicationOpen nowSenior Data ScientistDevsinc · Lahore, Punjab, Pakistan

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on Devsinc's own form.

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The clock on this job

Early applications get read.

12.8% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.2%1 day
  2. 4.5%3 days
  3. 12.8%7 days
  4. 20.6%14 days
  5. 35.6%30 days
  6. 58.4%60 days
This job: posted 28 hours ago

The posting

We are looking for a skilled and results-driven Senior Data Scientist with 3+ years of professional experience in data science, machine learning, artificial intelligence, and predictive analytics. The ideal candidate should have strong expertise in Python, machine learning frameworks, statistical modeling, and AI-powered tools, with the ability to transform complex datasets into actionable business insights.

The candidate will be responsible for developing, training, evaluating, and deploying machine learning models, leveraging modern AI tools and frameworks to build scalable, data-driven solutions. Experience with Generative AI, Large Language Models (LLMs), and cloud-based AI services will be considered a strong advantage.

Key Responsibilities

  • Collect, clean, process, and analyze structured and unstructured datasets to identify patterns, trends, and business opportunities.
  • Develop, train, evaluate, and optimize machine learning and deep learning models for real-world applications.
  • Build predictive models, classification and regression systems, recommendation engines, and forecasting solutions.
  • Use Python libraries such as Pandas, NumPy, Scikit-learn, Matplotlib, and Seaborn for data analysis, visualization, and model development.
  • Work with machine learning and deep learning frameworks, including TensorFlow, PyTorch, and Keras.
  • Leverage AI tools, Generative AI, LLMs, and prompt engineering techniques to develop intelligent applications and automate workflows.
  • Apply statistical analysis, hypothesis testing, feature engineering, and model validation techniques to improve model performance.
  • Develop data pipelines and integrate machine learning models into production applications through APIs and backend services.
  • Collaborate with software engineers, data engineers, product managers, and business stakeholders to deliver AI-driven solutions.
  • Monitor deployed models, evaluate performance, identify data drift, and implement improvements.
  • Document technical processes, model architectures, experiments, and analytical findings.
  • Stay updated with emerging AI technologies, machine learning methodologies, and industry best practices.

Requirements

  • Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Mathematics, or a related field.
  • Strong proficiency in Python, Pandas, NumPy, and Scikit-learn.
  • Hands-on experience with Machine Learning, Deep Learning, TensorFlow, or PyTorch.
  • Knowledge of Generative AI, LLMs, prompt engineering, RAG, and AI tools/frameworks such as LangChain, LlamaIndex, or Hugging Face.
  • Strong understanding of statistics, data analysis, feature engineering, and model evaluation.
  • Experience with SQL, data visualization tools, and REST APIs using FastAPI or Flask.
  • Familiarity with AWS, Azure, or GCP, Docker, Git, and MLOps tools such as MLflow.
  • Excellent analytical, problem-solving, and communication skills.

Preferred Qualifications

  • Experience with Generative AI, LLMs, RAG (Retrieval-Augmented Generation), embeddings, and vector databases.
  • Familiarity with AI frameworks and tools such as LangChain, LlamaIndex, Hugging Face, OpenAI APIs, or equivalent technologies.
  • Exposure to cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform (GCP).
  • Knowledge of MLOps practices, model monitoring, CI/CD pipelines, MLflow, or similar experiment-tracking tools.
  • Experience processing large datasets and working with data engineering pipelines.
  • Familiarity with NLP, computer vision, recommendation systems, or time-series forecasting
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