AI/ML Analyst

1 week ago

Bengaluru, Karnataka, India LogIQ Labs Full time ₹ 12,00,000 - ₹ 24,00,000 per year

Exploratory Data Analysis (EDA):
Dive deep into large, complex datasets to identify trends, patterns, and anomalies. Formulate hypotheses that can be tested with ML models.

Model Development & Validation:
Design, train, and validate machine learning models (e.g., classification, regression, clustering, forecasting) to address business problems like

  • Insight Generation:
    Go beyond model accuracy. Analyze model results, perform feature importance analysis, and interpret outputs to provide clear, actionable insights and business recommendations.
  • Data Visualization:
    Create compelling dashboards and reports (e.g., in Tableau, Power BI, or Python libraries) to communicate complex analytical findings and model performance to business stakeholders.
  • Data Wrangling:
    Write efficient and complex SQL queries to extract and prepare data for analysis. Use Python (Pandas) for advanced data cleaning, transformation, and feature engineering.
  • Collaboration:
    Work closely with product managers, business analysts, and data engineers to define project requirements, gather data, and ensure analytical solutions are well-integrated and effective.
  • Performance Monitoring:
    Track and monitor the performance of deployed models, identifying opportunities for retraining or refinement.

Required Qualifications & Skills

  • Experience:
    3-5 years of hands-on experience in a data analytics, business intelligence, or data science role with a clear focus on machine learning.
  • Education:
    Bachelor's degree in a quantitative field such as Computer Science, Statistics, Mathematics, Economics, or Engineering.
  • Technical Skills:
  • Python:
    High proficiency, including mastery of data science libraries (
    Pandas, NumPy, Scikit-learn
    ).
  • SQL:
    Expert-level ability to write complex queries (e.g., window functions, CTEs, joins) for data extraction and manipulation.
  • Machine Learning:
    Strong practical experience with a variety of ML techniques (e.g., Linear/Logistic Regression, Random Forests, Gradient Boosting, K-Means Clustering).

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