• Develop, launch, and nurture new digital marketing businesses.• Create innovative online marketing sales plans.• Exhibit expertise in various digital marketing solutions (SEO, SEM, PPC, social media, ORM, etc.).• Deliver impactful presentations to clients, showcasing website-related services for US and Canada locations (SEO, SEM, PPC, social media, ORM, etc.).• Maintain accurate and up-to-date sales pipelines and forecasts.• Conduct comprehensive business analysis for clients.• Utilize industry-standard tools and technologies for website audits (e.g., Audit Report, Siteliner), SEO analysis (e.g., Semrush), and website performance evaluation (e.g., Page Insights, Wappalyzer).• Establish and maintain strong relationships with clients through regular calls and virtual meetings.

Spearhead initiatives to fortify the placement department of the educational institution, recognizing its pivotal role in shaping the institution's reputation and annual admissions.Execute strategies aimed at optimizing student placements, acknowledging the direct correlation between placements and institutional success. Employ classical machine learning techniques, including Data Exploration, Data Cleaning, Feature Engineering, Model Building, and Model Testing, within the "Campus Placement Prediction" project to forecast student recruitment in campus placements.Implement various machine learning algorithms tailored to the specific requirements of the "Campus Placement Prediction" project. Utilize cloud platforms for model deployment to ensure scalability and accessibility for the "Campus Placement Prediction" project. Develop an API or create a user interface for model testing within the context of the "Campus Placement Prediction" project, facilitating seamless integration and ease of use.Produce High-Level Design (HLD) and Low-Level Design (LLD) documents outlining the solution strategies for the "Campus Placement Prediction" project.Design system architecture in wireframe and architecture documents to provide a comprehensive overview of the structure and functionality of the "Campus Placement Prediction" project.

--> Gather technical roles (requirements) from clients such as Tata, Cognizant, and Microsoft.--> Utilize LinkedIn Recruiter Premium portal to identify candidates that match the requirements of clients.--> Use Python, Selenium, WebDriver, and Pandas to scrape candidate details such as name, address, and designation, and create a structured DataFrame.--> Generate Excel sheets for the collected candidate data.--> Refer the curated candidate data to the recruitment team.--> Scrape data of other company recruiters relevant to the Business Development Manager(BDM) team to facilitate client deals.--> Compile the scraped recruiter data into Excel sheets.--> Tools utilized: Python, Selenium, WebDriver, Jupyter Notebook, and Google Colab.

Identify and source potential candidates through various channels, including online job boards, social media, professional networks, and referrals.Review resumes and applications to evaluate candidates' qualifications and match them with job requirements. Conduct initial phone screenings to assess candidates' skills, experience, and cultural fit with the company.Schedule and coordinate interviews between candidates and hiring managers ortechnical teams.Attend industry events, conferences, and meetups to network with potential candidates and stay connected with the tech community.Stay updated on emerging technologies and trends in the tech industry to better understand candidate skillsets and market demands.

Data Collection | Feature Engineering | Feature Selection | Model CreationAssisted in data preprocessing tasks by cleaning, formatting, and transforming raw data to make it suitable for analysis, learning the importance of data quality and consistency.Contributed to EDA efforts by generating basic statistical summaries and visualizations to gain insights into data distributions and trends, aiding in initial data understanding.Supported senior data scientists in evaluating machine learning models by calculating common metrics like accuracy and providing input on model performance. Assisted in documenting analysis processes and findings.Gained exposure to feature engineeringtechniques by assisting in the creation of new variables or features to improve model predictive power.Contributed to data visualization tasks by creating simple charts and graphs using tools like Matplotlib or Seaborn, aiding in the communication of data-driven insights to team members.

As a Data Science intern, my main responsibilities include collecting and preprocessing data, analyzing and modeling data using statistical techniques and machine learning, conducting experiments and tests to evaluate models, creating visualizations and reports to communicate findings, collaborating with cross-functional teams, staying updated with the latest trends in data science, documenting my work and sharing knowledge, adhering to ethical considerations, solving problems, and focusing on my professional development. These tasks provide me with valuable hands-on experience, networking opportunities, and a chance to contribute to real-world data science projects.

* Understanding and analyzing the clientsmanpower requirements in different skill set categories * Formulation and Implementation ofRecruitment/Sourcing strategies* Sourcing resumes through Employee Referralsand Social Networks like LinkedIn, Face book.* Posting of job on the job portals, websites, blogs and other discussion forums* Using sourcing strategies like Referral generation, Direct Sourcing, Networking, Mass Mailing* Using Online Database (Job Portals) & Offline Database to find out technically fit resources* Doing niche-skill and specific resource-search through Headhunting & Mapping Techniques * Screening the resumes and interacting withcandidates.* Telephonic Screening of the profiles - Checking for Technical Fitment, Behavioral Fitment,Stability * Conducting preliminary interviews as specified by client* Scheduling of Interviews of the candidates inclose co-ordination with client HR Ensuring properco-ordination between Clients and Candidates.* Evaluating the candidate on various parameters like communication skill, personality and assessing the candidate by grading them* Articulating and negotiating job offers with candidates-including the salary, benefits, bonuses, relocation, etc* Follow-up with candidates for Post Employment Reference Checks, Notice Period Negotiations, etc till joining.