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Data Analyst (AI/ML)

3 years

8 - 12 Lacs

noida uttar pradesh india

Posted:1 day ago| Platform: Linkedin logo

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Skills Required

data ai ml analysis learning drive strategy efficiency query analyze regression clustering forecasting engineering model sql extraction python numpy communication visualization reports tableau power collaboration

Work Mode

On-site

Job Type

Full Time

Job Description

We are seeking a curious and analytical

AI/ML Data Analyst

to join our growing team. This role is the perfect blend of deep data analysis, statistical modeling, and applied machine learning. With 3+ years of hands-on experience, you will be responsible for translating complex business questions into data-driven solutions.You will work cross-functionally to mine massive datasets, perform exploratory data analysis, build predictive models, and generate actionable insights that drive product strategy, operational efficiency, and business growth. The ideal candidate has a strong statistical foundation and proven experience in applying ML models to real-world business problems.

Key Responsibilities

  • Exploratory Data Analysis (EDA): Query, clean, and analyze large, complex datasets to identify trends, patterns, and anomalies.
  • Predictive Modeling: Develop, train, and evaluate machine learning models (e.g., regression, classification, clustering, time-series forecasting) to solve business problems like customer churn prediction, fraud detection, or sales forecasting.
  • Feature Engineering: Identify and create relevant features from raw data to improve model accuracy and performance.
  • Data Manipulation: Write efficient and complex SQL queries for data extraction. Use Python (Pandas, NumPy) for advanced data manipulation, cleaning, and preparation.
  • Insight Generation & Communication: Translate complex statistical and ML-driven findings into clear, concise, and actionable insights for non-technical stakeholders.
  • Visualization: Create compelling dashboards and reports (using tools like Tableau, Power BI, or Python libraries) to communicate model results and analytical insights.
  • Model Validation: Continuously monitor and validate model performance in production, suggesting improvements and retraining as needed.
  • Collaboration: Partner with data engineers, product managers, and business stakeholders to define project requirements, gather data, and deploy solutions.
Skills:- Artificial Intelligence (AI) and Machine Learning (ML)

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