Bishnu Prasad Sahu

Ex Implementation Specialist Intern at Simetrik | Ex AI Lab Intern at COE NIC | B.Tech in Computer Science at KIIT · Bhubaneswar, Odisha, India

I'm a Technical Consultant and Implementation Specialist with 5 years of experience bridging the gap between complex financial systems and actionable business solutions, currently driving impact at Simetrik. I hold myself and my work to a high standard — combining sharp analytical thinking with a grounded, empathetic approach to ensure every stakeholder feels heard and every implementation lands with precision. Fueled by ambition and a strong sense of ownership, I thrive at the intersection of financial reconciliation, business analytics, and numerical optimization, turning technical challenges into clear, scalable outcomes.

Experience

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Implementation Specialist - Technical Consultant

Simetrik · Jun 2025 – Sep 2025

Not yet confirmed

• - Supported the implementation and optimization of automation solutions for financial data workflows, ensuring efficiency and accuracyin client operations.
• - Gained hands-on experience with Simetrik’s automation ecosystem, completing certifications in Automatizar, Gestionar, and Auditar,which strengthened expertise in process automation, management, and auditing.
• - Collaborated with cross-functional teams to troubleshoot technical challenges, streamline deployment, and deliver value-drivensolutions to enterprise clients.
• - Developed problem-solving, client engagement, and consulting skills while adapting to fast-paced, real-world business requirements.

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AI Lab Intern

National Informatics Centre, Govt of India · May 2025 – Jul 2025

Not yet confirmed

Intern

Unified Mentor · Dec 2024 – Jan 2025

Not yet confirmed

• The internship was focused on the application of machine learning methodologies, using Random Forest Classifiers among others, in solving some real-world problems such as employee attrition.
• This includes handling a project from end-to-end, starting with the exploration and preprocessing of a dataset, where categorical variables are transformed using one-hot and binary encoding to make sure that the model is correctly represented.
• Feature engineering and data manipulation covered the merging of data frames and the conversion of categorical features into their numerical formats.
• Quite significantly, much emphasis was given to feature importance evaluation in order to find the key determinants of employee turnover, including monthly income, age, and years of service.
• It also underlined the problem of handling unbalanced datasets and treatments that the model results would need to provide actionable insight.
• Technical ability in Python, Pandas, NumPy, and Scikit-learn skills were key toward the implementation and adjustment of machine learning models.
• More than purely technical aspects, the work demands an in-depth understanding of workforce analytics, how predictive models may be used, and how the insights from data can help solve business problems.

Developer Cohort 5

Salesforce · Nov 2023 – Jan 2024

Not yet confirmed

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Host

International Conference ESMAC-2022 · Nov 2022 – Dec 2022

Not yet confirmed