Ankita Tiwari

ML Engineer

https://www.opentalent.in/ankita-tiwari

Madhav Institute Of Technology & Science

Key Strengths

Strong foundational knowledge in Machine Learning, Deep Learning, Computer Vision, and Natural Language Processing.
Demonstrated ability to build end-to-end ML pipelines from data preprocessing to deployment using various frameworks (Scikit-learn, TensorFlow, PyTorch, Streamlit, Flask).
Practical experience through multiple complex projects (Resume Parser, Face Recognition, Recommendation Systems, Prediction Systems) showcasing application of diverse ML algorithms and techniques.
Proficiency in Python, SQL, and core CS concepts like DSA and OOP.
Projects indicate a problem-solving orientation and ability to deliver functional applications with stated accuracy metrics.
The candidate's skills and project experience align well with the technical requirements for an ML Engineer role, particularly at an entry to mid-level.

Cultural & Operational Fit

Cultural Fit Analysis

Based on extracurricular activities like participating in CODETHON and volunteering, the candidate demonstrates engagement in team-oriented and competitive environments, which could indicate a positive cultural fit. However, the psychometric test score is 0, meaning there is no data to objectively assess logical reasoning, work attitude, stress handling, or team collaboration. Therefore, a definitive cultural fit analysis cannot be provided based on the given assessment data.

Soft Skills & Operational Fit

The candidate's summary indicates a desire to solve real-world problems in a growth-oriented organization, suggesting a proactive and learning-oriented mindset. Extracurricular achievements (swimming, coding competition, volunteering) suggest discipline, competitive drive, problem-solving abilities, and teamwork skills. However, without actual psychometric test results, a comprehensive assessment of work attitude, stress handling, and team collaboration is not possible. The provided psychometric test score is 0, indicating no data for evaluation.

AI is analyzing your overall score…

Identifying your key strengths…

Evaluating your skill match against the job requirements…

Assessing your cultural and operational fit

Key Strengths

Strong foundational knowledge in Machine Learning, Deep Learning, Computer Vision, and Natural Language Processing.
Demonstrated ability to build end-to-end ML pipelines from data preprocessing to deployment using various frameworks (Scikit-learn, TensorFlow, PyTorch, Streamlit, Flask).
Practical experience through multiple complex projects (Resume Parser, Face Recognition, Recommendation Systems, Prediction Systems) showcasing application of diverse ML algorithms and techniques.
Proficiency in Python, SQL, and core CS concepts like DSA and OOP.
Projects indicate a problem-solving orientation and ability to deliver functional applications with stated accuracy metrics.
The candidate's skills and project experience align well with the technical requirements for an ML Engineer role, particularly at an entry to mid-level.

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit

Ankita Tiwari

Key Strengths

Cultural & Operational Fit

Skills

Education

Key Strengths

Cultural & Operational Fit