Tata Advanced Systems Ltd. (TASL) is a well-positioned company of the Tata Group, which addresses the Government of India’s initiatives to spur indigenous development and manufacturing of critical aerospace and defense solutions. TASL is progressively the partner of choice for global aerospace & defense companies.
The company has multiple capabilities across the aerospace value chain, be it design & engineering, industrialization, tooling, parts fabrication, and assembly. It has recently started India’s first military radar assembly facility in the private sector for the Indian Navy with six focus areas involving aerospace, UAVs, missiles, radars, command & control, and homeland security.
TASL is a single source of aerostructures for major global aerospace companies and has won the contract for India’s first private-sector military radar production for the Ministry of Defense.
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The talent acquisition team of Tata Advanced Systems Ltd. wanted to recruit job-fit fresh graduates through off-campus hiring for the role of Mechanical and Software Engineers.
The TA team was looking for candidates proficient in aptitude, coding, and technical knowledge. This was time-consuming and did not provide an accurate evaluation of the candidate's technical and coding abilities.
The TASL team was impressed by iMocha’s customized skill assessment library. The assessments were aimed at hiring fresh graduates virtually. The evaluation was based on aptitude and technical skills with the use of a mixed set of questions provided by both the TASL team and iMocha’s question bank.
The TASL team found that the use of advanced analytics made the process of shortlisting easier and consumed 50% lesser time, as compared to the manual procedure followed before. iMocha’s assessment also improved hiring efficiency by 40% and helped TASL select the right candidates with the right skill sets.
iMocha’s automated recruitment solution helped TASL to successfully shortlist 80% of the candidates for the final round. They observed that the overall recruitment efficiency had been optimized by 40%, selecting the most relevant candidates proficient in technical knowledge and aptitude. With regard to actual numbers, 45 candidates out of 200 fresh graduates selected initially had appeared for the final round.
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