Consultant - Forensics - National - ASU - Forensics - Investigations & Compliance - Gurgaon

Job Description: Data Analyst (SQL, Python, Visualization, Machine Learning)

Role and Responsibilities:

• Collaborate with cross-functional teams to identify data requirements, analyze business processes, and recommend data-driven solutions.
• Develop and execute complex SQL queries to retrieve, clean, and transform data from various sources, ensuring data accuracy and reliability.
• Utilize Python for data manipulation, statistical analysis, automation tasks, and implementing machine learning models.
• Apply machine learning techniques for predictive modeling, anomaly detection, and pattern recognition to derive actionable business insights.
• Perform exploratory data analysis to uncover trends, patterns, and anomalies within datasets.
• Create insightful and interactive visualizations using tools like Tableau, Power BI, or Python libraries (matplotlib, seaborn).
• Develop and optimize machine learning pipelines, including feature engineering, model selection, and hyperparameter tuning.
• Translate analytical findings into actionable recommendations for stakeholders, supporting data-driven decision-making.
• Present data-driven insights to both technical and non-technical audiences, effectively communicating complex concepts.
• Collaborate with data engineers and scientists to improve data quality, infrastructure, and scalability of analytical solutions.

Qualifications:

• Bachelor’s degree in computer science, Statistics, Data Science, or related field.
• Proven 3+ experience as a Data Analyst or Data Scientist or Product Analyst.
• Strong proficiency in SQL for querying and manipulating data from relational databases.
• Proficiency in Python for data manipulation, analysis, and scripting.
• Experience with data visualization tools (Tableau, Power BI, matplotlib, seaborn) to create impactful visual representations.
• Solid understanding of statistical concepts and data analysis techniques.
• Strong problem-solving skills with an ability to work with large, complex datasets.
• Excellent communication skills to convey insights and findings to various stakeholders.
• Ability to work independently and collaborate effectively within a team environment.
• Experience with machine learning concepts.

Job Description: Data Scientist (Machine Learning, NLP, Image Analytics)

Role and Responsibilities:

• Lead and contribute to end-to-end data science projects, from problem formulation to model deployment.
• Apply advanced machine learning algorithms and techniques to solve business challenges, with a focus on predictive modeling, classification, and regression.
• Develop and fine-tune NLP models for text analysis, sentiment analysis, entity recognition, and language generation.
• Utilize image analytics techniques to extract valuable information and patterns from images and videos.
• Collaborate with cross-functional teams to gather and understand data requirements, and translate them into analytical solutions.
• Clean, preprocess, and transform data to ensure accuracy and reliability for modeling purposes.
• Identify key insights and trends from data, and communicate findings to both technical and non-technical stakeholders.
• Build and evaluate machine learning models, ensuring their performance and robustness.
• Contribute to the improvement of existing models and algorithms, as well as the development of new approaches.
• Collaborate with data engineers to deploy models into production environments.
• Stay up-to-date with the latest trends and advancements in machine learning, NLP, and image analytics.

Qualifications:

• Bachelor's degree in Computer Science, Data Science, Statistics, or related field.
• 3+ years of hands-on experience in data science, machine learning, NLP, and image analytics.
• Proficiency in machine learning frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, spaCy, NLTK.
• Strong programming skills in Python for data manipulation, model development, and scripting.
• Demonstrated expertise in NLP techniques, including sentiment analysis, named entity recognition, and text classification.
• Experience with image analytics tools and libraries, such as OpenCV, Pillow, and deep learning frameworks.
• Strong understanding of statistical concepts, data analysis, and feature engineering.
• Proven ability to analyze complex datasets and extract actionable insights.
• Excellent problem-solving skills and a creative mindset for designing innovative solutions.